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The numerical solution of dynamic problems for porous fluid-saturated solids is often performed with the use of simplified equations known as the u-p approximation. The simplification of the equations consists in neglecting some acceleration terms, which is justified for a certain class of problems related, in particular, to geomechanics and earthquake engineering. There exist two u-p approximations depending on how many acceleration terms are neglected. All comparative studies of the exact and u-p formulations are focused on the question of how well the u-p solutions approximate those obtained with the exact equations. In this paper, the equations are compared from a different point of view, addressing the question of well-posedness of boundary value problems. The exact equations must be hyperbolic and satisfy the corresponding hyperbolicity conditions for the boundary value problems to be well posed. The u-p equations are not of the form to which the conventional definition of hyperbolicity applies. A slight extension of the approach makes it possible to derive hyperbolicity conditions as necessary conditions for well-posedness for the u-p approximations. The hyperbolicity conditions derived in this paper for the u-p approximations are formulated in terms of the acoustic tensor of the skeleton. They differ essentially from the hyperbolicity conditions for the exact equations.

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure.

The climate crisis is urging us to act fast. Buildings are a key leverage point in reducing greenhouse gas (GHG) emissions, but the embodied emissions related to their construction often remain the hidden challenge of any ambitious policy. Therefore, in this paper, we explored material GHG neutralization where herbaceous biobased insulation materials with negative net-global warming potentials (GWPs) were used to compensate for building elements that necessarily release GHGs. Different material diets, as well as different building typologies, were modeled to assess the consequences in terms of biobased insulation requirements to reach climate neutrality. Our results show that climate-neutral construction can be built with sufficient energy performance to fulfill current standards and with building component thicknesses within a range of 1.05–0.58 m when timber- and bamboo-based construction is chosen. Concrete-based ones require insulation sizes that are too large and heavy to be supported by the dimensioned structures or accepted by urban regulations. Moreover, a time horizon of 20 years is more appropriate for assessing the contribution of material shifts to biobased materials in the transition period before 2050. This paper demonstrates that this is technically feasible and that climate neutrality in the construction sector just depends on the future that we choose., Environmental Science & Technology, 56 (8), ISSN:0013-936X, ISSN:1520-5851

Computerisation and digitalisation are shaping the world in fundamental and unpredictable ways, which highlights the importance of computing education research (CER). As part of understanding the roots of CER, it is crucial to investigate the evolution of CER as a research discipline. In this paper we present a case study of a Finnish CER conference called Koli Calling, which was launched in 2001, and which has become a central publication venue of CER. We use data from 2001 to 2020, and investigate the evolution of Koli Calling's scholarly communities and zoom in on it's publication habits and internalisation process. We explore the narrative of the development and scholarly agenda behind changes in the conference submission categories from the perspective of some of the conference chairs over the years. We then take a qualitative perspective, analysing the conference publications based on a comprehensive bibliometric analysis. The outcomes include classification of important research clusters of authors in the community of conference contributors. Interestingly, we find traces of important events in the historical development of CER. In particular, we find clusters emerging from specific research capacity building initiatives and we can trace how these connect research spanning the world CER community from Finland to Sweden and then further to the USA, Australia and New Zealand. This paper makes a strategic contribution to the evolution of CER as a research discipline, from the perspective of one central event and publication venue, providing a broad perspective on the role of the conference in connecting research clusters and establishing an international research community. This work contributes insights to researchers in one specific CER community and how they shape the future of computing education

The rapid growth of the e-commerce market leads us to expect a further increase in delivery vehicles in urban areas as well. This growth is expected to be accompanied by an increase in emissions while space becomes scarce. Meanwhile, people are adjusting their travel behaviour; therefore, the growing e-commerce market affects both last-mile delivery and private passenger traffic. Failed home deliveries are an important factor. They produce additional traffic by both ”Courier, Express and Parcel” (CEP) service providers and private passengers in the form of repeated delivery attempts or trips to pick up parcels. In this paper we apply an integrated agent-based model of last-mile deliveries and private travel demand. This allows for analysis of interactions between delivery and private passenger traffic and the status of the recipients during delivery. Furthermore, we present a neighbourship model to account for deliveries accepted by neighbours, which is crucial to reproduce realistic delivery success rates. We applied the presented model to the city of Karlsruhe, Germany, and simulated multiple delivery policy scenarios, which we compare to a static model without interactions between private and delivery agents. Our results show that the agent-based model produces more nuanced success rates with respect to different socio-demographic groups. Differentiating these groups is necessary when assessing measures that target specific groups and analysing effects of demographic changes. Also, we show the necessity of considering neighbours in such a model. This paper provides insight into the effects of e-commerce on a transport system and a framework to analyse policy measures or alternative delivery strategies.

Energy efficiency, sustainability, and renewable energy sources are becoming increasingly relevant topics in today’s world. Buildings are one of the largest consumers of energy in society, and as such, improving their energy efficiency by reducing unnecessary energy loss and utilizing solar power is crucial. This paper comprehensively analyzes a neighborhood with buildings characteristic of the researched area by applying empirical and theoretical methods and calculations that have been proven in numerous individual cases. The main contribution of this paper is its demonstration that implementing methods to increase the energy efficiency of buildings and utilizing the potential of solar power can result in significant savings in energy consumption, increase the energy sustainability of the analyzed buildings, and substantially reduce the negative environmental impact. The novelty of this study lies in the location and multiple software applications for data analysis. The data and conclusions obtained in this paper serve as a foundation and path towards sustainable development in the field of energy efficiency for buildings in this and similar areas. Heat loss was calculated by analyzing households in the urban neighborhood of Nova Kolonija. All analyzed houses exceeded the maximum allowed annual required energy for household heating (75 kWh/m2). After the reconstruction and implementation of the proposed measures to increase energy efficiency, all houses met the requirements and entered a higher energy class, C. Energy for heating was reduced from 9294.68 kWh/a to 4641.84 kWh/a, representing a reduction of 50.03%. Simulations were conducted regarding solar rooftop power plant installation of 5655 Wp capacity. Software that was used included: a Photovoltaic Geographical Information System (PVGIS), Photovoltaic System software (PVsyst 7.3.), and Photovoltaic Design and Simulation software (PV*SOL). The results of the analysis indicated that the average amount of electricity produced is 6186.98 kWh, which meets 98.12% of households’ annual electricity consumption of 6278.41 kWh. The paper contributes to the existing body of literature and provides significant insight for both practical implications and future studies.

In line with the decarbonization of the domestic sector to meet the 2050 climate neutrality targets, this paper describes the energy, economic, and environmental analysis of a set of different novel configurations of polygeneration installations to provide electricity, air conditioning, domestic hot water, and desalinated water for a building of 80 dwellings. All arrangements were designed to cover 100% of the five demands required in the building with renewable energy only, from photovoltaic (PV) and photovoltaic-thermal (PVT) panels and biomass backup boilers (BB). Electricity can be sold to or purchased from the grid without electrical storage with batteries. Additional electricity generation with thermoelectric generators (TEG) coupled to the PVTs, and the BB was explicitly analyzed. The choice of electrically or thermally activated technologies (heat pump, HP/single-effect absorption chiller, SEAC for cooling and multi-effect distillation, MED/reverse osmosis, RO for desalination) created four configurations from the basic structure based on solar and biomass sources. Thus, the paper has studied four designs in detail and applied them to three case studies corresponding to different locations in Spain. They were modeled with TRNSYS and included specific models for desalination technologies. Both structures provide important energy and CO2 savings concerning the conventional supply of the building demands. The novel life-cycle analysis approach further increases the lifetime CO2 savings for all configurations as well. The electric option (the combination of HP and RO for cooling and desalting) was, by far, the most attractive solution in terms of liability and lower investment required in the three case studies.

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively.

Naturally buckling steel braces (NBBs) have been recently developed by the authors and co-workers to improve the buckling performance and energy dissipation capacity of braced framed structures. In NBBs, a low-yield-point steel (LYS) channel and a high-strength steel (HSS) channel are connected using steel battens to build up a dual-material steel section. An intentional eccentricity is introduced along the brace length to subject the brace to bending loads in addition to axial loads. Previous experiments have demonstrated that this combined axial-flexural response stabilizes the compression behaviour of the brace and enhances its tensile post-yielding stiffness through a novel deformation mechanism. In this paper, the cyclic behaviour of two full-scale NBB specimens with different section sizes and eccentricities are investigated experimentally. Gusset plate pin-connections that accommodate in-plane buckling are used to release the brace ends from high ductility demands. Two low-cycle fatigue protocols with increasing amplitudes and repeated inelastic loading cycles at the event of local buckling are adopted. Test results show that both NBB specimens exhibited a stable hysteresis behaviour by delaying the onset of local buckling up to a 1.5 % story drift ratio (SDR). Notably, the specimen with larger section and larger eccentricity provided a stable tensile strength under five repeated loading cycles of 2.0 % SDR. An equivalent damping ratio of 0.4 was measured. In addition to the experimental research, a computational study is performed with the aid of the finite element software ABAQUS to evaluate partially strengthening method of the sections against local buckling. It was found that the energy dissipation capacity of NBBs can be enhanced up to 40% by using rib stiffeners at critical locations, while the use of thicker channel battens can provide further restrain to local buckling growth up to a 3.0% SDR. The paper develops the physical equations to support an analytical hysteretic model for predicting the force-displacement cyclic relationship of chevron NBBs. The accuracy and targeted conservatism of the proposed hysteretic model is confirmed through comparisons with the test results.

Increasing implementation of energy storage systems (ESS) is an available way to mitigate the fluctuation of renewable energy sources (RES) in smart grid. This paper proposes distributed control strategies without/with event-triggered information delivery for a group of heterogeneous energy storage systems (HESS) with different parameters such that the power fluctuation of renewable generation and load variation can be smoothed, meanwhile ensuring power and energy sharing among various ESS. To mitigate the impacts of parameter differences in HESS, a weighted control matrix is designed, and the method of deriving optimal feedback control gain is designed with the help of Riccati control theory. Aiming at reducing communication burdens meanwhile guaranteeing system performance, an event-triggered information delivery strategy without Zeno behavior is introduced to the distributed control method. Convergence and stability of the proposed methods are verified by rigorous mathematical analysis. Simulation studies are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can achieve the state-of-charge (SoC), the state-of-temperature (SoT) and the state of-energy (SoE) balance of HESS, proportional power sharing and power tracking objectives, even if the states of HESS are interfered by random disturbances.

PurposeOnly recently have historic concrete buildings received attention and the need for their protection has been understood. Their listing as architectural heritage in most countries is ruled by legislations. The research carried out within the framework of the CONSECH20 JPI project on the conservation of historic concrete buildings in the Czech Republic, Cyprus, Italy and the Netherlands has allowed to study the legislations in the four aforementioned countries and how these are brought to practice. This paper aims at the evaluation of these legislations and of their function in practice.Design/methodology/approachThe legislations have been examined focussing on the protection of historic buildings and the guidelines to achieve a correct technical conservation. These were assessed in practical situations. The situations of the four countries were studied and the parameters used allowed comparisons.FindingsConcrete buildings are at risk and the guidelines should be further developed to meet actual conservation needs, including historical and aesthetical compatibility. The re-use of listed concrete buildings often means transforming and adapting these to a variety of modern needs and norms: the complexity of this assignment asks for a multidisciplinary teamwork. The bottom-up Dutch programme for quality in conservation, striving to bring ethical and technological principles to practice, could be a sound basis for developing respectful conservation strategies of heritage concrete buildings.Research limitations/implicationsThe research concerns the four countries involved in the CONSECH30 project and could be extended to include more countries.Practical implicationsMore stakeholders have to be involved in the process of conservation and transformation of heritage concrete buildings. This should be directed by the legislation.Social implicationsNo direct social implications are foreseen from the outcome of the research. However, the suggestion is made that social involvement is essential in planning concrete building transformations.Originality/valueThe study focussed on the application of theory (the legislation) to practice (thus showing the limits of the legislation), which is an innovative way of contributing to the conservation of historic concrete buildings.

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either.

This paper investigates the challenges and opportunities for assigning spectrum for private 5G networks, with particular emphasis on the 3.5 GHz band and regulation issued by the Danish spectrum authority Energistyrelsen. We are chiefly interested in the dilemma between providing sufficient and clean spectrum for a private network versus ensuring that high network density can be supported. Indoor and outdoor scenarios are considered, and the performance impact of interference on different levels of service availability are investigated. We develop and propose new solutions for enhanced spectrum regulation options leveraging native 5G features, such as bandwidth part, to support denser outdoor and indoor deployments that can enhance best effort traffic and simultaneously protect spectrum for critical and delay sensitive traffic. System-level simulations show that our proposals can protect critical services and significantly increase the capacity per network in dense deployments.

Recently, with the establishment of new thermal regulation, the energy efficiency of buildings has increased significantly, and various deep learning-based methods have been presented to accurately forecast the heating load demand of buildings. However, all of these methods are executed on a dataset with specific distribution and do not have the property of global forecasting, and have no guarantee of data privacy against cyber-attacks. This paper presents a novel approach to heating load demand forecasting based on Cyber-Secure Federated Deep Learning (CSFDL). The suggested CSFDL provides a global super-model for forecasting heating load demand of different local clients without knowing their location and, most importantly, without revealing their privacy. In this study, a CSFDL global server is trained and tested considering the heating load demand of 10 different clients in their building environment. The presented results, including a comparative study, prove the viability and accuracy of the proposed procedure.

The numerical modelling of the torsion behaviour of masonry block interfaces is a key aspect for the assessment of the out-of-plane response of masonry walls. Nevertheless, it still represents a challenging computational issue due to the high non-linear coupling between the torsion and other internal forces (shear, bending moment and axial load), which rigorously requires the adoption of complex 3D non-linear constitutive laws. Some limit analysis-based approaches, proposed in the specific literature, represent efficient and reliable numerical tools for predicting the ultimate states of brick masonry structures subjected to combined in-plane and out-of-plane actions, while involving complex torsion loads and combined actions. This paper originally introduces a simplified discrete inelastic interface able to simulate the non-linear torsion-shear behaviour of masonry contact joints, within the context of static incremental analysis. The more general mechanical behaviour of the interface is ruled by six degrees of freedom and is governed by four rotating links (RL), whose actual orientation is updated during the step by step analysis, by taking into account the current position of the twisting centre of the interface. The incremental torsion-shear capacity curve and the corresponding ultimate domains obtained by the proposed model are compared with the ultimate load limit analysis predictions and with some experimental data available in the literature. The results highlight the ability of the new discrete interface to effectively reproduce the full non-linear behaviour of masonry contact interface subjected to different loading combinations.

Wheat straw, in the form of compacted bales, is increasingly used as thermal insulation in the external walls of buildings. Common practice is to use a render finish, applied directly to surface of the straw bales, to protect them from decay, and enhance structural performance and fire resistance. Coatings are typically made of water vapour permeable materials, such as lime or earth-based renders. Such coatings should allow water vapour to diffuse through, minimising the risk of liquid moisture build up within the thickness of the wall, reducing likelihood of decay. However, to date there has been very limited scientific study of this behaviour in rendered straw bale walls. The aim of the work presented in this paper was to develop understanding of the hygrothermal performance of lime rendered wheat straw bales. A test panel was subjected to varying environmental conditions, including a thermal shock, dynamic freeze–thaw exposure and hot humid conditions. Key scientific contributions of this work include data on the dynamic and steady-state hygrothermal characteristics wheat straw bale walls, combined with the application of heat and moisture modelling. This work will further support uptake of straw bale construction by designers and their wider use in energy-efficient construction projects.

Building thermal modeling is the founding stone upon which numerous carbon reduction strategies in the building sector are built. Yet, as of today, little to no interpretable and calibrated models founded on real-world measurements have been open-sourced. This work attempts to remedy this deficiency and renders public improved results of a recently published stochastic model identification of building heat dynamics study evaluated over 225 Dutch residential buildings. Calibrated lumped resistance-capacity models are made available, along with thermal characterizations of the buildings and reported meta-data. The paper discusses how open-access building thermal models support a collection of building service applications such as building performance benchmarks, model-based control, demand-side management, policy impact assessment, and data augmentation. Insights provided present a starting point for open access benchmarks of building thermal dynamics, paving the way toward new scientific discoveries from common standards.

Smart buildings aim to increase energy performance while maintaining or improving the indoor livingconditions. A digital twin of a smart building should be able to provide a virtual representation of the actual building behavior in real-time through accurate simulations considering both the air quality and energy related performance. Such digital twin setups are useful for investigating the building behavior in a non-invasive manner and for validating the effectiveness of novel management and control algorithms under realisticconditions before their actual deployment in real buildings. This paper presents the key functional requirements that a digital twin platform for building should satisfy. Then, an architecture is proposed for the development of a digital twin for buildings while design guidelines are also provided. The architecture includes a software platform for the integration of smart algorithms and user interfaces to enable the intelligent operation of buildings. The digital twin captures the indoor environmental conditions by modeling the operation of theHeating, Ventilation and Air-Conditioning (HVAC) system, the occupants’ behavior, the building’s thermal insulation, and the outdoor weather conditions. At the same time, the building electricity consumption is characterized by modelling the load consumption according to users’ habits, the HVAC operation, the renewable energy generation, and the weather conditions. An example for the development, validation, and calibration of a digital twin of an actual building is demonstrated in this paper.Furthermore, some use cases are demonstrated through the software platform to investigate how an operational fault or anactual battery system, connected in a hardware in the loop configuration, can affect the indoor environmental conditions and the energy performance of the building.&nbsp

Knowing the position of a moving target can be crucial, for example when localizing a first responder in an emergency scenario. In recent years, ultra wideband (UWB) has gained a lot of attention due to its localization accuracy. Unfortunately, UWB solutions often demand a manual setup in advance. This is tedious at best and not possible at all in environments with access restrictions (e.g., collapsed buildings). Thus, we propose a solution combining UWB with micro air vehicles (MAVs) to allow for UWB localization in a priori inaccessible environments. More precisely, MAVs equipped with UWB sensors are deployed incrementally into the environment. They localize themselves based on previously deployed MAVs and on-board odometry, before they land and enhance the UWB mesh network themselves. We tested this solution in a lab environment using a motion capture system for ground truth. Four MAVs were deployed as anchors and a fifth MAV was localized for over 80 second at a root mean square (RMS) of 0.206 m averaged over five experiments. For comparison, a setup with ideal anchor position knowledge came with 20 % lower RMS, and a setup purely based on odometry with 81 % higher RMS. The absolute scale of the error with the proposed approach is expected to be low enough for applications envisioned within the scope of this paper (e.g., the localization of a first responder) and thus considered a step towards flexible and accurate localization in a priori inaccessible, GNSS-denied environments.

Buildings can become a significant contributor to an energy system’s resilience if they are operated in a coordinated manner to exploit their flexibility in multi-carrier energy networks. However, research and innovation activities are focused on single-carrier optimization (i.e., electricity), aiming to achieve Zero Energy Buildings, and miss the significant flexibility that buildings may offer through multi-energy coupling. In this paper, we propose to use blockchain technology and ERC-1155 tokens to digitize the heat and electrical energy flexibility of buildings, transforming them into active flexibility assets within integrated multi-energy grids, allowing them to trade both heat and electricity within community-level marketplaces. The solution increases the level of interoperability and integration of the buildings with community multi-energy grids and brings advantages from a transactive perspective. It permits digitizing multi-carrier energy using the same token and a single transaction to transfer both types of energy, processing transaction batches between the sender and receiver addresses, and holding both fungible and non-fungible tokens in smart contracts to support energy markets’ financial payments and energy transactions’ settlement. The results show the potential of our solution to support buildings in trading heat and electricity flexibility in the same market session, increasing their interoperability with energy markets while decreasing the transactional overhead and gas consumption.

The use of solar photovoltaic (PV) generation and battery energy storage (BES) systems in commercial buildings has been increasing significantly in recent years. Most of these systems, however, are designed to solely minimize the investment and operation costs. With the increasing concerns about high-impact low-probability (HILP) events, such as natural disasters, and their impact on power system resilience, there is a substantial need to integrate outage risks in power system infrastructure planning problems. This paper examines the impact of various electricity rates on the feasibility of PV and BES systems in commercial buildings for financial and resilience purposes. Simulation studies are conducted using the Renewable Energy Integration & Optimization (REopt) decision support software to optimize the size of solar PV and BES systems for both financial and resilience purposes, considering different combinations of geographic locations, load profiles, electricity rates, and outage durations. The feasibility assessment is conducted by analyzing and comparing the net present value (NPV) for each combination of parameters.

Optimization and decision-making tools are often utilized to enhance the performance of community buildings under extreme events, but this may compromise the ability of future generations to enhance performance. Hence, a sustainability-oriented approach is required to enhance the performance of community buildings under extreme events. In this context, this paper proposes an optimization and decision-making framework considering multiple performance indicators, including socioeconomic and environmental consequences as well as retrofit costs. These performance indicators are assessed by utilizing performance-based assessment methodologies in terms of sustainability dimensions. The performance indicators are then exploited as multiple performance objectives in a genetic optimization to determine the Pareto optimal solutions. Finally, the Pareto optimal solutions are utilized for decision making to extract ideal solutions for the given retrofit costs. The ideal solutions provide trade-offs between the consequences of extreme events and the retrofit costs required to reduce the consequences of extreme events.

The protection of the stone surfaces of the buildings of the city of Lecce (Apulia, Italy) represents an ancient practice, which has always allowed the conservation of the historical-artistic heritage of the city, which nowadays is an international touristic and cultural destination. The identification of ancient recipes, materials and methodologies for the protection of historical buildings plays an important role in establishing correct protocols in order to ensure the durability of stone surfaces over time. This work presents a historically accurate reconstruction of the materials and conservation technologies used on the facades of the artistic buildings in Lecce. Several historical buildings, both civil and religious, have been selected in order to investigate the treatments applied on their facades and to know the traditions spread in the past in the field of building conservation in the Salento territory. Thanks to non-invasive or micro-destructive techniques (optical microscopy, ATR-FTIR spectroscopy, pyrolysis–gas chromatography–mass spectrometry), the characteristic molecular markers of the materials and the products of degradation have been identified, deepening the knowledge of the mechanisms of deterioration and interaction between the stone material, the surface finish and the surrounding environment. The paper is a valuable tool for the knowledge of ancient traditions and the planning of proper restoration works.

The paper describes a case study on the design of district energy infrastructure for the green-field project of the Vale de Santo António (Lisbon, Portugal). The methodology is based on a novel framework built on the sequential integration of three software tools: QGIS, City Energy Analyst (CEA) and Urbio. QGIS is used to define the building database (construction solutions, uses and schedules); CEA is used to model the neighborhood energy services (heating, cooling, domestic hot water and electricity, including for EVs); and Urbio is used to design the energy infrastructure to supply the neighborhood in an optimal manner. The study illustrates the high impact of building renovation on heat demand and the existing potential for a two-layout network to minimize pumping demand and heat distribution loss. Moreover, a sensitivity analysis with regard to future carbon tax changes and electricity greening concludes that an electricity-based heating mix is optimal.

Unintended Infiltration in buildings is responsible for a significant portion of the global housing stock energy demand. Today, the fan pressurization method, also known as blower-door test, is the most frequently used measurement method to evaluate the airtightness of buildings and determining the total air change rate of a building or a building element. However, the localization and quantification of single leaks in the building envelope remain difficult and time-consuming. In this paper, an acoustic method is introduced to estimate the leakage size of single leaks in buildings. Sound transmission measurements and measurements of airflow have been conducted in a laboratory test apparatus. The objective of this investigation is to compare acoustic measurements with airflow measurements of leaks under the same boundary conditions. The test apparatus consists of two chambers, which are separated by a test wall. This test wall represents a single characteristic air leakage path in the building envelope. Various types of wall structures with different slit geometries, wall thicknesses and insulation materials have been investigated. The acoustic measurements have been performed with a sound source placed in one chamber and ultrasonic microphones located in both chambers. The results of the acoustic measurements were compared to airflows through the test wall measured using a flow nozzle to provide estimates of the uncertainty in the acoustic approach.

The generation of mathematical models for the analysis of buildings with multiple thermal zones is a large and complex task. Furthermore, the order and complexity of the dynamical model are increased by the number of included thermal zones. To overcome this problem, this paper presents an algorithm to define the mathematical model automatically, using the geometric and physics parameters as inputs. Additionally, the spatial position of each thermal zone must be recorded in an arrangement called a contact matrix. The algorithm for modeling systems with multiple thermal zones is the main contribution of this work. This algorithm is presented in pseudocode format and as an annex, an implementation in MATLAB software. One of the advantages of this methodology is that it allows us to work with parallelepipeds and not necessarily cubic thermal zones. The algorithm allows us to generate mathematical models with symbolic variables, starting from the knowledge of how many thermal zones compose the system and its geometric organization. This information must be organized in a matrix arrangement called a contact matrix. Different arrays of thermal zones were constructed with wooden boxes to verify the functionality of the models generated with the algorithm. Each case provided information that allowed us to adjust the mathematical models and their simulations, obtaining a range of errors between experimental and simulated temperatures from 2.08 to 5.6, depending on the number of thermal zones studied.

An expected increase in the use of air conditioning by 2050 will significantly increase electricity demand and come at a cost to the environment. Implementing passive cooling strategies and focusing on personal environmental control systems (PECSs) could help to address this issue. While numerous studies have investigated the positive impact of PECSs on thermal comfort and energy savings, their overall economic benefit has been poorly addressed. We present an economic evaluation of personal fans for an office building in Germany. Building performance simulation was used to compare passive and active cooling concepts, and sensitivity analysis was performed for different climate scenarios. A cost-benefit analysis was carried out, including an assessment of investment and operating costs and the monetary value of relative performance. The transferability of comfort and productivity into costs is the novelty of this paper. The results showed that by supplementing night ventilation with personal fans, discomfort hours could be reduced by up to 50%. However, the initial investment of the fan is not compensated by savings in productivity losses compared to night ventilation alone. A reduction in the cost of the technology could help to economically offset the investment. The results contribute to the literature on the economic evaluation of a PECS by proposing a framework to motivate its implementation in buildings.

With the recent surge in interest in microbial prevention, this review paper looks at the different antimicrobial technologies for surfaces in the built environment. Every year, more than 4 million people are at risk of dying due to acquiring a microbial infection. As per the recent COVID-19 pandemic, such infections alone increase the cost and burden to the healthcare system. Therefore, mitigating the risk of microbial infection in the built environment is one of the essential considerations in our preparedness for future pandemic situations. This is especially important for a dense population within urban cities and for indoor environments with higher concentrations of indoor contaminants due to poorer ventilation. The review assesses antimicrobial technologies developed in the last two years and their potential and suitability for implementation on surfaces within a building, and it also suggests key considerations when developing these technologies for a built environment. The keywords in the main search include “antimicrobial”, “coating”, and “surfaces”. The work found various studies describing the potential use of antimicrobial technologies for different material surfaces. Still, a more thorough investigation and upscaling of work are required to assess their suitability for built environment applications. The widely diverse types of built environments in public areas with their varying purpose, design, and surfaces also mean that there is no “one-size-fits-all” solution for every space. In order to improve the adoption and consideration of antimicrobial surfaces, the built environment industry and stakeholders could benefit from more in-depth and long-term evaluation of these antimicrobial technologies, which demonstrate their real-time impact on various built environment spaces. Published version This research was funded by The British Council Newton Fund grant number 2020- RLWK12-10630 and The APC was funded by The British Council Newton Fund.

Digitalization and sustainability are twin trends in the construction industry (CI). Mobile devices are becoming more and more powerful, gaining the ability to support tasks that, until recently, were unthinkable. One example is LiDAR. The use of this technology embedded in tablets and smartphones brings new possibilities at a scan-to-BIM level. Given the commitment to circularity, waste audits will become mandatory for renovation projects. This is a heavy task that can benefit from existing processes and from digital technologies. This paper aims to demonstrate how everything is related and can be efficiently combined. The research convenes digital transition, green transition, circular economy, waste audit, LiDAR, and design processes’ improvement. A mixed-methods approach aims to demonstrate how it is possible to establish digital waste audits and how these can become the core of the diagnosis process to be made during strategic definition or as part of the renovation design’s initial actions. LiDAR technology embedded in mobile devices is worthy and can bring major improvements to the diagnosis phase. This will become more compliant with objectives set for digitalization and improved sustainability, contributing to the sector’s twin digital and green transitions.

Although one of the main aims of using renewable energy sources in building applications is to reduce the environmental impact caused by the high global energy demand of buildings, it can also produce other positive effects, known as co-benefits. Thermal energy storage technologies are often used in building applications, either integrated into the renewable system or independently, for energy savings or energy efficiency reasons. This paper demonstrates that it is possible to identify the co-benefits of the use of thermal energy storage in buildings by cross-sectorizing the renewable energy and thermal energy storage sectors. To this end, this article first reviews the literature on the co-benefits of renewable energy for building applications, followed by an evaluation on how these co-benefits can be attributed to thermal energy storage in buildings. As a result of a keywords analysis, the main co-benefits of thermal energy storage were identified related to environmental, health, economic, cost, and policies aspects. This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE) and by the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors would like to thank the Catalan Government for the quality accreditation given to their research group GREiA (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme.

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business continuity of companies active in these industries. However, companies often face severe resource limitations when implementing these plans for all of their physical assets. Therefore, those critical physical assets that are vital for providing their key products should be identified. Moreover, sustainability and resilience are inseparable parts of organizations’ strategies, including strategic asset management plans. Therefore, any proposed ranking methodology for physical asset prioritization should encompass sustainability and resilience measures to ensure its practicality. This paper proposes a novel framework for physical asset criticality analysis based on the so-called business impact analysis to ensure the continuity of providing products/services through the continuity of physical assets. A hybrid fuzzy BWM-TOPSIS method is first applied to identify the key products. Then, a hybrid fuzzy DEMATEL-Bayesian network is applied based on proper sustainability and resilience factors to determine the critical physical assets, while interdependencies among these factors are well captured. The normalized expected asset criticality index is defined to guide managers in taking appropriate directions while developing asset management plans. A case study of a gas company is provided to show the applicability of the proposed decision model. The data needed for each step of the framework is gathered through experts’ judgments, historical data available on the sites, or quantitative risk assessment scenarios.

Cities around the world increasingly recognize the need to build on their resilience to deal with converging forces such as disasters, environmental degradation, urban sprawl, and climate change. Given the significance of critical infrastructure networks (CINs) for maintaining the quality of life in buildings and communities, improving their resilience is of high importance to governors, planners, and policymakers. Therefore, this study is proposed to spatially analyze the resilience of CINs in Saudi Arabia and to develop a holistic resilience framework for buildings and communities. The research method of this study involves a case study of Najran, including a collection of CIN data, history of natural disasters, and future potential hazards. The data were compiled to list the basic parameters required for the development of resilience criteria. Overall results show that CINs in the city of Najran were performing below average compared with the measurement criteria. The study highlights the need to make improvements in terms of the robustness, redundancy, and flexibility of the CINs in the city. Moreover, this paper proposes a holistic framework of key aspects of resilient criteria that need to be taken into account by the city governor, policymakers, and developer bodies for better management of available infrastructure and its development in future years.

Over the last ten years, due to the increase in frequency and severity of climate change effects, resilience in buildings has become a growing topic in the current global discussion on climate change adaptation. Designing both sustainable and resilient constructions would help to face such effects; however, sustainability and resilience in design have been mostly treated separately so far. Since sustainability has been considered more than resilience, paying deeper attention to the latter is indispensable to reducing building vulnerability. The purpose of this article is to examine the commonalities between the sustainability and resilience of buildings using two different approaches: (i) a systematic literature review, taking into consideration a 10-year period for selecting records, and (ii) an analysis of five green building rating systems and five resilience rating systems and guidelines selected according to their popularity and number of certified buildings. There is an overlap in some indicators between the two domains at the building level, as shown by the results from both paths. These aspects could assist in considering sustainability and resilience from the very beginning of the design process. This will ensure that buildings may be designed more effectively by considering and enhancing the synergies between the two domains. This paper targets potential stakeholders who may be interested in including such an integrated implementation in their designs.

This paper presents a rapid seismic assessment and Fibre Reinforced Polymer (FRP) retrofit design methodology which relies on the European design guidelines recently published in Chapter 8 of fib Bulletin 90 on the use of externally applied FRP reinforcement in the seismic retrofitting of reinforced concrete (r.c.) structures. For this purpose, an example-guide is developed with step-by-step hand calculations aiming to facilitate engineers of practice and researchers working in the field to easily understand the proposed methodology. A three-storey, pilotis-type residential r.c. building is selected typical of the Mediterranean construction practice in the 1970s. The methodology followed only aims to provide preliminary results on seismic assessment and retrofitting before the implementation of more sophisticated analysis if need be (e.g., in case of irregular buildings). The assessment procedure identified that the columns of the ground storey, being the most critical structural elements for the stability of the structure, are vulnerable to brittle failure modes. To remove all the brittle failure modes attributed to inherent deficiencies and enhance the overall deformation capacity of the building, the strengthening schemes applied in the ground storey (pilotis) is a combination of local strengthening measures, such as FRP wrapping, and global interventions. The latter may refer to the addition of r.c. jacketing to the central column to remove slenderness and of metal X-braces to modify the lateral deflection shape of the building and thus moderate the interstorey displacement demand.

Concrete structures are increasingly becoming exposed to organic acid attack conditions, such as those found in agriculture and food-related industries. This paper aims to experimentally verify the thermodynamic modeling of cement pastes under acetic acid attack. For this, a modeling approach implemented in IPHREEQC via Matlab is described, and results are compared with measured pH and compositions of equilibrated solutions (MP-AES) as well as unreacted/precipitated solids (XRF, XRD and STA) for a wide range of acid concentrations. The 11% replacement of cement by silica fume (SF) led to a 60 or 70% reduction (measured or modeled, respectively) of Portlandite content in the hardened cement paste due to the pozzolanic reaction resulting in higher content of CSH phases, which has effects on the progression of dissolution processes and a resulting pH with increased acid concentrations. Considering that no fitting parameter was used, the model predictions showed good agreement with measured values of pH, dissolved ion concentrations and composition of the remaining (degraded) solids overall. The discrepancies here were more pronounced at very high acid concentrations (equilibrium pH < ~4), i.e., after the full dissolution of hydrate phases due to limitations in the model used to describe Al-, Si- and Fe-gel phases and/or identified experimental challenges in precipitation of calcium and aluminum acetate hydrates.

This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historical I/O data. Given only output measurements, the inputs are estimated by the UIE, which is shown to asymptotically converge to the true input without knowing the initial conditions. Both open-loop and closed-loop UIEs are developed based on Lyapunov conditions and the Luenberger-observer-type feedback, whose convergence properties are studied. An experimental study is presented demonstrating the efficacy of the closed-loop UIE for estimating the occupancy of a building on the EPFL campus via measured carbon dioxide levels.

Summarization: This paper is concerned with the impacts of smart lane changes of connected and automated vehicles (CAVs) on their own travel performance as well as the entire traffic flow. Based on MOBIL and reinforcement learning, two ego-efficient lane-changing strategies were developed in this work to enable lane-changing decisions for CAV s to improve their travel efficiency. The MOBIL approach intends to establish such a lane-changing strategy by optimizing MOBIL's two parameters, while the reinforcement learning approach tries to develop such a strategy from scratch using Q-learning with sufficient traffic environmental information. The lane-changing strategies were developed and compared on the basis of intensive microscopic traffic simulation. In addition, the information impact on the performance of the reinforcement learning approach was examined to determine the essential amount of environmental information required. Παρουσιάστηκε στο: 2021 IEEE International Intelligent Transportation Systems Conference

Using data from simulated and actual case studies, this paper assesses the accuracy and precision of Bayesian estimates for the constructional date of medieval masonry buildings, generated from the radiocarbon evidence returned by different assemblages of wood-charcoal mortar-entrapped relict limekiln fuel (MERLF). The results from two theoretical studies demonstrate how Bayesian model specifications can be varied to generate a chronologically continuous spectrum of distributions from radiocarbon datasets subject Inbuilt Age (IA). Further analysis suggests that the potential for these distributions to contain the date of the constructional event depends largely upon the accuracy of the latest radiocarbon determination within each dataset, while precision is predicated on dataset age range, dataset size and model specification. These theoretical studies inform revised approaches to the radiocarbon evidence emerging from six culturally important Scottish medieval masonry buildings, each of which is associated with a wood-charcoal MERLF assemblage of different botanical character. The Bayesian estimates generated from these radiocarbon datasets are remarkably consistent with the historical and archaeological evidence currently associated with these sites, while age range distributions suggest the IA of each MERLF assemblage has been constrained by the taxa-specific and environmentally contingent lifespans and post-mortem durabilities of the limekiln fuel source. These studies provide further evidence that Bayesian techniques can generate consistently accurate chronological estimates for the construction of medieval masonry buildings from MERLF radiocarbon data, whatever the ecological provenance of the limekiln fuel source. Estimate precision is contingent upon source ecology and craft technique but can be increased by a more informed approach to materials analysis and interpretation.

Terrestrial laser scanning (TLS) is a leading technology in data acquisition for building information modeling (BIM) applications due to its rapid, direct, and accurate scanning of different objects with high point density. Three-dimensional point cloud classification is essential step for Scan-to-BIM applications that requires high accuracy classification methods, running at reasonable processing time. The classification process is divided into three main steps: neighborhood definition, LiDAR-derived features extraction, and machine learning algorithms being applied to label each LiDAR point. However, the extraction of LiDAR-derived features and training data are time consuming. This research aims to minimize the training data, assess the relevance of sixteen LiDAR-derived geometric features, and select the most contributing features to the classification process. A pointwise classification method based on random forests is applied on the 3D point cloud of a university campus building collected by a TLS system. The results demonstrated that the normalized height feature, which represented the absolute height above ground, was the most significant feature in the classification process with overall accuracy more than 99%. The training data were minimized to about 10% of the whole dataset with achieving the same level of accuracy. The findings of this paper open doors for BIM-related applications such as city digital twins, operation and maintenance of existing structures, and structural health monitoring.

Following the huge economic losses and building damage caused by yearly flooding in China, increased attention to flood risk management within the urban and suburban areas is required. This paper provides an example of the flood risk management of suburban buildings in Anji County. The temporal and spatial characteristics of inundation in the study area are simulated and analyzed based on a verified coupled hydrodynamic model. The vulnerability curve of local masonry buildings to flood risk is established from the theory of structural static mechanics and the empirical equation of flood load. According to the consequences of the hydrodynamic model and vulnerability curve, a flood risk assessment of suburban buildings is conducted. The results show that severe inundation will occur once the dikes are broken. In the 20-, 50-, and 100-year return periods, there are, respectively, 43, 286 and 553 buildings at extremely high risk, distributed in almost each building region. Over half involved buildings are high risk. Buildings at low-lying lands should worry about the great hydrostatic actions caused by terrible waterlogging. This approach can be popularized in urban, suburban, and rural areas, aimed at frame, masonry and even informal structure. The results can provide a scientific reference for Anji County to reduce the flood loss and enhance the flood resistance.

Progress monitoring is an essential part of large construction projects. As manual progress monitoring is time-consuming, the need for automation emerges, especially as, nowadays, BIM for the design of buildings and laser scanning for capturing the as-built situation have become well adopted. However, to be able to compare the as-built model obtained by laser scanning to the BIM design, both models need to use the same reference system, which often is not the case. Transforming the coordinate system of the as-built model into the BIM model is a specialist process that is pre-requisite in automated construction progress monitoring. The research described in this paper is aimed at the automation of this so-called registration process and is based on the dominant planar geometry of most buildings with evident corner points in their structures. After extracting these corner points from both the as-built and the design model, a RANSAC-based pairwise assessment of the points is performed to identify potential matching points in both models using different discriminative geometric invariants. Next, the transformation for the potential matches is evaluated to find all the matching points. In the end, the most accurate transformation parameter is determined from the individual transformation parameters of all the matching corner points. The proposed method was tested and validated with a range of both simulated and real-life datasets. In all the case studies including the simulated and real-life datasets, the registration was successful and accurate. Furthermore, the method allows for the registration of the as-built models of incomplete buildings, which is essential for effective construction progress monitoring. As the method uses the standard IFC schema for data exchange with the BIM, there is no loss of geometrical information caused by data conversions and it supports the complete automation of the progress-monitoring process.

Over the last few decades, energy efficiency has received increasing attention from the Architecture, Engineering, Construction and Operation (AECO) industry. Digital Twins have the potential to advance the Operation and Maintenance (O&M) phase in different application fields. With the increasing industry interest, there is a need to review the current status of research developments in Digital Twins for building energy efficiency. This paper aims to provide a comprehensive review of the applications of digital twins for building energy efficiency, analyze research trends and identify research gaps and potential future research directions. In this review, Sustainability and Energy and Buildings are among the most frequently cited sources of publications. Literature reviewed was classified into four different topics: topic 1. Optimization design; topic 2. Occupants’ comfort; topic 3. Building operation and maintenance; and topic 4. Energy consumption simulation. This research was funded by the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), grant number 22/2551-0000574-8.

The definition of ambitious life cycle-based benchmarks and target values to limit the GHG emissions of buildings is seen as one of the most important steps in pushing the construction and real estate sector in significantly reducing its contribution to global warming. Especially target values are no longer only developed from a bottom-up perspective. There is now an interest by governments and sustainability assessment system providers in supplementing bottom-up approaches with science-based top-down approaches as part of their responsibility to respect planetary boundaries. The creation of GHG emission budgets in combination with target values, as well as the introduction of strict enough legal binding requirements already today is critical for achieving a climate-neutral building stock. Achieving these tasks requires tackling still open methodological issues. Following the work of IEA EBC Annex 72 and current developments in Germany, the paper presents main questions, key steps, modelling aspects that can cause variation and uncertainties, as well as clarifies key terms and definitions. It is highlighted that although a net zero emission requirement is a universal benchmark, information on system boundaries and calculation rules are still necessary to provide evidence of its fulfilment.

Under seismic actions, stone masonry buildings are prone to damage. To assess the severity of damaged masonry buildings and their failure modes, engineers connect these problems to surface crack features, such as the crack width and the extent of cracking. We aim to further these assessments in this study, wherein we propose using simple machine learning models to predict: 1) three ratios encoding the degradation of stiffness, strength, and displacement capacity of damaged rubble stone masonry piers as a function of the observed crack features and the applied axial load and shear span ratio; and 2) the pre-peak vs. post-peak regime, based on the crack features. When predicting the stiffness, force, and drift ratios, the prediction error is significantly reduced when the axial load and shear span ratio are included in the feature vector. Furthermore, when predicting the pre-peak vs. post-peak regime, simple machine learning models such as the k-nearest neighbor and the logistic regression result in remarkable accuracy. The obtained results have significant implications on the automated post-earthquake assessment of masonry buildings using image data. It is shown based on documented laboratory test data, that, by selecting proper crack features and incorporating information about the kinematic and static boundary conditions, even simple machine learning models can predict accurately the damage level caused to a rubble masonry pier. The three crack features used in this study are the maximum crack width, length density, and complexity dimension. The pipeline developed in this paper is general enough and is applicable to other masonry typologies and elements upon new evaluation of crack features and image data., Automation in Construction, 140, ISSN:0926-5805

The methods for preventing earthquake-induced structural pounding between two adjacent buildings include ensuring a sufficient separation distance between them or decreasing their relative displacement during seismic excitation. Some equations or even specific values of such gap sizes between two buildings have been introduced so as to avoid collisions. Increasing the stiffness of buildings, using tuned mass dampers, applying liquid dampers, or decreasing the mass of the structures may reduce lateral displacements, and therefore pounding can be prevented. On the other hand, the application of base isolation systems may result in the elongation of the natural period of the building, thus increasing the probability of structural pounding. The aim of the present paper is to verify the effectiveness of using rubber bumpers to reduce the negative effects of earthquake-induced pounding between base-isolated buildings. The analysis was conducted for different gap sizes between buildings, as well as for various values of the thickness, number and stiffness of rubber bumpers. The results of the study show that the peak impact force decreases with increasing thickness, stiffness, and number of bumpers. Moreover, the peak impact forces are reduced with increasing gap size. The results of the investigation clearly indicate that the use of additional rubber bumpers can be considered an effective method for reducing the negative effects of earthquake-induced pounding between base-isolated buildings.

The rational use of energy systems is one of the main discussions in sustainability in the 21st century. Water pumping systems are one of the most significant consumers of electricity in urban systems, whether for urban water supply, sewage, or use in vertical buildings. Thus, this work aims to present Industry 4.0 (I4.0) technologies applied in buildings’ water pumping systems, focusing on energy efficiency, supervision, and control of the pumping system. The work involves four steps: (i) identifying the existing I4.0 technologies and (ii) mapping the possibilities of applying Industry 4.0 technologies in building pumping systems. The study includes the analysis of (16) articles published in journals between 2018 and June 2021 to identify I4.0 technologies cited in the publications. It identified and grouped eighteen (18) technologies based on twenty-two (22) terms observed in the papers. The study classified the identified technologies into three possible applications in a building water pumping system. The applications include: (i) directly applicable, (ii) partially applicable, and (iii) application not yet identified. Therefore, the study presents the advantages of I4.0 technologies developed primarily for the industry sector, also applicable in residential building water pumping systems. These technologies’ benefits include energy efficiency, user control, a reduction from periods of failure of the pumping system (maintenance), water quality, and moving towards Intelligent Pumping or Pumping 4.0.

The operation of buildings is significant among the human activities that withdraw water from nature, and evaluating the water efficiency of buildings is essential for sustainable development. Hence, this paper aims to assess the water efficiency in school buildings to identify benchmarks that could be used as targets in water-saving initiatives alongside highlighting which type of variable is more influential for each building. A cluster benchmarking system was developed and applied to 82 public school buildings in Florianópolis, Brazil. Data were obtained from the state water supply company and both state and municipal education departments. Water consumption drivers were defined through a literature review and the language R was used for clustering the sample. Water efficiency was then evaluated using suitable indicators for occupation conditions, building rooms and spaces and water appliances. High and low-efficiency buildings were identified in the five clusters generated through the k-means algorithm. Schools with excessively low or high consumption that could be related either to the under-measurement or leaks were identified, which is useful for water network management. In conclusion, water-efficient school buildings were highlighted as benchmarks and the type of variables that should be addressed for enhancing the accuracy of water-saving initiatives were highlighted.

International audience; This paper deals with the mechanical and fire behaviour of simple solutions of thermal breaks for external steel structures (balconies, passageways, solar panels …) attached on a concrete facade with external thermal insulation. The proposed solutions are composed of a PVC or plywood layer implemented between an extended end-plate connection and the concrete support (lintel, floor slab, concrete wall). A total of 12 monotonic mechanical tests have been performed on steel-to-concrete connections using intermediate insulation layers (PVC or plywood) in order to investigate the rotational stiffness, plastic/ultimate bending moments, the rotation capacity as well as the failure mode. Two types of fastening were used; bolted embedded end-plates and post-installed mechanical/chemical fasteners. In order to evaluate the influence of the studied solutions of thermal breaks on the behaviour of a balcony under fire, two external flame experimental tests were carried out on realistic balconies attached to a concrete support. These fire tests demonstrated that the implementation of the proposed thermal breaks between end-plates and the support does not affect the stability of the balconies subjected to fire. © 2021

This paper explores, by means of continuum finite element (CFE) analysis, a recently developed concept that promotes controlled inelastic energy dissipation within the embedded portion of column base connections rather than in steel columns of conventional embedded column bases. As such, dissipative embedded column base connections become resilient to local buckling. Several loading and geometric parameters are interrogated through parametric simulations with a high-fidelity CFE model, which is thoroughly validated with available experimental data. The simulation results suggest that steel columns in conventional embedded bases are prone to local buckling even at modest lateral drift demands (i.e., 2% rad), thereby exhibiting 10 to 30 mm of axial shortening in the presence of constant compressive axial load. On the other hand, dissipative bases are resilient to local buckling till at least 4% rad lateral drift demands regardless of the column cross-sectional characteristics and loading parameters. At larger lateral drift demands, the use of horizontal stiffeners in dissipative bases alleviates localized flange deformations attributable to bearing of the embedded column flange with the reinforced concrete foundation. Axial shortening in dissipative base connections is only attributable to inelastic cyclic straining within the dissipative zone and is up to 60% less than that of steel columns in conventional bases at lateral drift demands of 2% rad. The CFE simulations reveal that the hysteretic response of dissipative bases is insensitive to bidirectional loading.

COVID-19 forced the human population to rethink its way of living. The threat posed by the potential spread of the virus via an airborne transmission mode through ventilation systems in buildings and enclosed spaces has been recognized as a major concern. To mitigate this threat, researchers have explored different technologies and methods that can remove or decrease the concentration of the virus in ventilation systems and enclosed spaces. Although many technologies and methods have already been researched, some are currently available on the market, but their effectiveness and safety concerns have not been fully investigated. To acquire a broader view and collective perspective of the current research and development status, this paper discusses a comprehensive review of various workable technologies and methods to combat airborne viruses, e.g., COVID-19, in ventilation systems and enclosed spaces. These technologies and methods include an increase in ventilation, high-efficiency air filtration, ionization of the air, environmental condition control, ultraviolet germicidal irradiation, non-thermal plasma and reactive oxygen species, filter coatings, chemical disinfectants, and heat inactivation. Research gaps have been identified and discussed, and recommendations for applying such technologies and methods have also been provided in this article.