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• There is an increase in the construction of supertall and megatall buildings. • Practical application of traditional surveying methods is limited by tower height. • Elevation changes may be monitored using strain measurements and prediction models. • Strain-based monitoring results agreed with conventional methods. • The strain-based method increased efficiency and decreased cost. Anticipated complexities associated with monitoring elevation changes during construction of the 326 m tall Salesforce Tower in San Francisco prompted the implementation of a strain-based system. This system utilizes strain gages embedded into the tower's reinforced concrete walls and the resulting measurements are inputs into one of three shortening estimation models of varying complexity and applicability. This methodology was successfully utilized to monitor time dependent shortening due to axial, shrinkage, and creep strains in the tower. Strain-gage based systems assist with slender tower construction in urban areas because the tower's slenderness coupled with adjacent obstacles stretch the limits of traditional or alternative instrument capabilities and they provide redundancy of measurement to ensure accuracy. This paper is a case study of how a strain-based elevation monitoring system helped this project to be successful. The most relevant findings are: 1) the shortening estimation models were in agreement with conventional survey measurements (±4.6 mm) within their recommended tower height and temperature range capabilities; 2) implementation of this system resulted in a net monitoring cost reduction on the order of 15 %, 400 less labor hours, and reduced potential for project delays; and 3) improvements are needed for supplying power to the sensors as there were interruptions, cut cables, and lack of sources that led to a 44 % sensor success rate. Given the need for structural health monitoring, the successful outcomes documented in this paper, and the potential for continued improvement by way of, for example, wireless and battery free sensing technologies, the work documented herein is believed to be an incremental advancement to the state-of-the-art of tall buildings. [ABSTRACT FROM AUTHOR]

For a long time, Polarimetric Synthetic Aperture Radar (PolSAR) data was not available free of cost, so the applications were limited. With the recent increase in availability of PolSAR data due to missions like ALOS-2, UAVSAR and Sentinel, the data can be acquired through these sensors periodically. Since the volume of data is large, applying traditional classifiers and conventional machine learning algorithms becomes confounding, as in that case, manual feature extraction must be done by the researcher for training the model. To automate this feature extraction step and accelerate the process, many researchers have used various deep learning algorithms. However, most studies fail to tap into the potential of PolSAR data by not utilizing the complete range of complex data of float-32 bit-depth. The work in this paper suggests the development of a generalized deep learning model for nine elements of coherency matrix [ T 3 ] in the float-32 data-space from ALOS/PALSAR-2 L-Band sensor. Semantic segmentation is used to classify land-covers into various classes like water, settlement, forest, open land, wetlands etc. This is done through modification on the UNet backbone. The deep learning model is trained using data on San Francisco and New Delhi regions and tested on data from Mumbai region. It is observed that the classification accuracy for Mumbai region is " 93.82 % ". This kind of system can assist the decision makers like urban planners to take informed decisions. [ABSTRACT FROM AUTHOR]

In this paper, we report a novel technique which enabled us to easily and directly obtain a silk fibroin (SF) solution without the use of organic solvents by applying a simple grinder for silk cocoons. In addition, we successfully prepared cellulose nanofiber/silk fibroin (CNF/SF) nanofiber composite films using a filtration and pressing method from a slurry solution blended with a CNF solution. The peak temperature of the thermal decomposition of the SF nanofibers prepared using the new method developed in this research was improved by more than 13 °C, and that of the composite films compounded with more than CNF 50 wt% showed almost the same temperature as the CNF film. In addition, the mechanical properties of the CNF/SF nanofiber composite films were increased with increasing the amount of CNF. These results strongly suggest that the SF solution prepared using our novel method has advantages of superior thermal and mechanical properties compared with that prepared by a conventional method using a concentrated neutral salt solution. Additionally, this result indicates the film manufacturing method having advantages of water pollution reduction and energy saving. Image 1 • Silk fibroin (SF) solution without use of organic solvents by a simple grinder. • Cellulose nanofiber (CNF)/SF fiber composites were developed. • CNF is effective as a reinforcing material for SF composite films. • CNF dramatically improves the heat resistance of SF film. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to rank building faults based on their impacts on the building energy penalty and occupant thermal comfort penalty considering multiple faults and fault occurrence rates. A fault impact analysis framework is created by incorporating the fault model library with the whole building energy performance simulation (e.g., EnergyPlus used in this study). The fault occurrence rate is introduced as a "meta" parameter in the simulation. This framework involves three essential aspects of conducting a fault impact analysis: fault constructing, fault simulation, and fault impact analysis. A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency, by using the deep-learning based response surface model (i.e., the multi-layer perceptron regression). The proposed fault analysis framework with ranking was tested and demonstrated for the DOE's prototype medium-sized office in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 12,000 EnergyPlus fault simulations. A total of 129 fault modes from 41 groups of fault models were simulated for the medium-sized office case. The results demonstrate the proposed framework is robust and scalable for the fault impact analysis. The top critical fault for the medium-sized office is the fault of HVAC-Left-ON for the packaged rooftop unit, regarding the site energy, source energy, and HVAC energy. Excluding the fault of HVAC-Left-ON, the top critical faults vary significantly among the four climate zones. [ABSTRACT FROM AUTHOR]

This paper evaluates through experimental testing a prototype of switchable insulated shading (SIS) systems suitable for windows to optimize solar heat gains in order to maintain indoor thermal comfort. Specifically, the paper describes a laboratory testing procedure and measurement results for SHGC values specific to various SIS designs and operation settings using a box calorimeter set-up. The SIS prototype can modulate continuously its optical and thermal properties including U-value and SHGC using rotating insulation layers controlled through an actuator to a specific slate angle setting. The results of the experimental testing confirm that SHGC value for the SIS prototype is correlated to the slate angle of the insulated layers. This correlation remains applicable for any SIS insulation thickness. The measured data are used to assess the potential energy efficiency benefits from SIS when applied to windows for US homes. In particular, a prototypical housing unit located in three US cities is considered to evaluate the energy efficiency potential for switchable opaque insulated shading systems. The results indicate that SIS systems can achieve up to 8.3%, 11.3%, and 11.4% in annual HVAC energy consumption compared to no-shading cases for dwellings located in Golden, CO (representing a cold climate), Phoenix, AZ (representing a hot climate), and San Francisco, CA (representing a mild climate), respectively. [ABSTRACT FROM AUTHOR]

The fast assembling of stiffness and mass matrices is a key issue in Isogeometric Analysis, particularly if the spline degree is increased. We present two algorithms based on the idea of sum factorization, one for matrix assembling and one for matrix-free methods, and study the behavior of their computational complexity in terms of the spline order p. Opposed to the standard approach, these algorithms do not apply the idea element-wise, but globally or on macro-elements. If this approach is applied to Gauss quadrature, the computational complexity grows as p d + 2 instead of p 2 d + 1 as previously achieved. • The paper contains a detailed complexity analysis of sum factorization (SF). • SF is applicable to tensor product discretizations of all kinds of PDEs. • SF provides the same matrix as standard assembling approaches based on Gauss quadrature. • SF performs best when applied globally or macro-element wise. • Parallel and fast implementations are provided. [ABSTRACT FROM AUTHOR]

• A novel bifurcate stent graft (BSG) with seamless tubular structure was developed successfully and which can avoid the leakage of traditional stent-graft. • A simple drug modification approach was conducted onto BSG surface using a mixture of heparin and silk fibroin solution. • The viability and attachment of human vascular smooth muscle (HVSM) cells cultured in the release of modified BSGs demonstrated that the modified BSGs could significantly inhibit the proliferation of HVSM cells. • The heparin-functionalized BSG with satisfactory thickness, water permeability resistance, sustainable drug release property and anti-occlusion function, which has potential applications in the treatment of vascular diseases. With the increase of vascular diseases in recent years, it is of importance to develop an anti-occlusion stent graft, which can meet the requirements of transplants for a long term. In this paper, we describe a silk fibroin (SF)/heparin-functionalized bifurcated stent graft (BSG) using textile forming technology. The BSGs were prototyped based on seamless weaving technology, and the surface was modified with SF-loaded heparin under steam/air treatment to improve their patency. The physical properties such as thickness, water permeability, contact angle, mechanical properties, and in vitro drug release and coagulation time of the BSGs were examined. The results showed that heparin modification can improve its coagulation time, and the water permeability resistance of the BSGs reached 1.154 ± 0.854 mL/(cm2×min), while their thicknesses were just 0.085 ± 0.004 mm. The heparin release of the BSGs showed that the release time was prolonged upon steam treatment by means of the increase in the β-sheet structure and crystallinity of SF. The viability and attachment of human vascular smooth muscle (HVSM) cells cultured in the release of modified BSGs demonstrated that the modified BSGs could significantly inhibit the proliferation of HVSM cells. The heparin-functionalized BSG with satisfactory thickness, water permeability resistance and anti-occlusion function, which has potential applications in the treatment of vascular diseases. [ABSTRACT FROM AUTHOR]

Abstract This paper first reports on the high-selective separation and recovery of Li(I) from Na(I) and K(I) using the polymer inclusion membrane (PIM) system containing both thenoyltrifluoroacetone (TTA) and trioctylphosphine oxide (TOPO) as the carrier molecules, and cellulose triacetate (CTA) as the base polymer. To characterize the PIM, we used Fourier Transform infrared (FT-IR) spectroscopy, scanning election microscopy (SEM) and thermogravimetric analysis (TGA). The optimized results of this work showed that initial flux ( J 0) of Li(I) that was as high as 1.04 × 10−2 mol m−2 h−1, corresponding to permeability (P) of 3.62 × 10−3 m h−1, were recorded using the PIM composed of 50 wt% CTA and 26.74 wt% TTA + 23.26 wt% TOPO from a feed phase containing Li(I), Na(I) and K(I) in an alkaline chloride solution into an acceptor phase containing 0.1 M HCl. Li(I) was transported successfully to the acceptor phase but Na(I) and K(I) barely passed in the first 20 h. The separation factor (S F) of Li(I) over the Na(I) and K(I) respectively (S F L i (I) / N a (I) ≒ 54.25, S F L i (I) / k (I) ≒ 50.60) demonstrated that Li(I) can be high-selectively separated. It is expected that this membrane can be applied for selective recovery of Li(I) from salt lakes or sea water that contains significant concentrations of Na(I) and K(I). Graphical abstract Image 1 Highlights • Promising functional PIM system for Li(I) recovery. • The carrier molecules including TTA and TOPO are adopted for PIM system. • Separation factors (SF): S F L i (I) / N a (I) & k (I) > 50. • Initial flux( J 0) of Li(I) is as high as 1.04 × 10−2 mol m−2 h−1. • Permeability(P) of Li(I) is 3.62 × 10−3 m h−1. [ABSTRACT FROM AUTHOR]

Climate changes brought about by global warming as well as man-made environmental changes are often the cause of sever natural disasters. ICT, which is itself responsible for global warming due to its high carbon footprint, can play a role in alleviating the consequences of such hazards by providing reliable, resilient means of communication during a disaster crisis. In this paper, we explore the provision of wireless coverage through UAVs (Unmanned Aerial Vehicles) to complement, or replace, the traditional communication infrastructure. The use of UAVs is indeed crucial in emergency scenarios, as they allow for the quick and easy deployment of micro and pico cellular base stations where needed. We characterize the movements of UAVs and define an optimization problem to determine the best UAV coverage that maximizes the user throughput, while maintaining fairness across the different parts of the geographical area that has been affected by the disaster. To evaluate our strategy, we simulate a flooding in San Francisco and the car traffic resulting from people seeking safety on higher ground. [ABSTRACT FROM AUTHOR]

Highlights • The performance of steel-concrete (SC) systems is enhanced through the use of hybrid fibers in the concrete core. • The hybrid fibers include a combination of macro and nano-scale fibers, steel fibers and carbon nanofibers respectively. • An overall fiber content percentage of 1.5% was deemed sufficient to alter the failure mode from brittle to ductile. • Experimental investigations on representative SC specimens confirmed the benefits gained from using hybrid fibers. Abstract Cementitious materials such as concrete are typically characterised as quasi-brittle with low tensile strength and low strain capacity, which hence affect the long-term durability of the structure. One of the most important issues in designing and maintaining massive concrete structures like offshore and nuclear power plants is concrete cracking, which is due to the low tensile strength of concrete. This can destroy the structural aesthetic and lead to deterioration of the structure. The addition of fibers to concrete has been proven to be a good mean to control its crack behaviour and maintain its ductility in tension. Further, since the discovery of carbon nanotubes/fibers (CNT/CNF), they have been also considered as efficient fibers for construction materials such as concrete. This study presents the structural performance of steel-concrete (SC) elements with a fiber reinforced concrete (FRC) core using both single and hybrid fibers (i.e. consisting of two types of fibers). For this study carbon nanofibers, and steel fibers which are conventionally used in practice, are used for the FRC. Static tests were conducted on eight SC beams with different concrete types. The paper reports on the experimental results obtained from four-point flexural loading of the SC beams. The study shows considerable improvement for both the strength and ductility of the tested specimens. The research laid the groundwork for additional in-depth studies on using carbon nanofiber reinforced concrete within structural members. [ABSTRACT FROM AUTHOR]

A high number of electric vehicles (EVs) in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation to overcome range anxiety and create a viable business model for charging stations (CSs). The framework must account for the stochastic nature of all stakeholders' operations, including EV drivers, CSs, and retailers and their mutual interactions. In this paper, a three-layer joint distributionally robust chance-constrained (DRCC) model is proposed to plan day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations for e-mobility ecosystems. The proposed three-layer joint DRCC framework formulates the interactions of the stochastic behaviour of the stakeholders in an uncertain environment with unknown probability distributions. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. An iterative process is proposed to solve the problem using joint DRCC formulation. To achieve computational tractability, the second-order cone programming reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of the temporal correlation of uncertain PV generation on CSs operation is considered in the formulation. A simulation study is carried out for an ecosystem of three retailers, nine CSs, and 600 EVs based on real data from San Francisco, USA. The simulation results show the necessity and applicability of such a scheduling framework for the e-mobility ecosystem in an uncertain environment, e.g., by reducing the number of unique EVs that failed to reach their destination from 272 to 61. In addition, the choice of confidence level significantly affects the cost and revenue of the stakeholders as well as the accuracy of the schedules in real-time operation, e.g., for a low-risk case study, the total net cost of EVs increased by 247.3% compared to a high-risk case study. Also, the total net revenue of CSs and retailers decreased by 26.6% and 10.6%, respectively. • Mutual interactions between stochastic parameters of stakeholders are investigated. • PV temporal correlations has be formulated in the optimization problem. • An exact second-order cone programming reformulation is presented to improve accuracy. • By increasing the confidence level, the cost of EVs and charging stations increased. • Number of EVs with constraint violation decreased by increasing confidence level. [ABSTRACT FROM AUTHOR]

This paper aims at developing a Historical Building Information Modelling methodology for supporting the diagnosis phase in historical constructions. To this end, the work evaluates the capacity of HBIM for integrating all the data generated during the pre-diagnosis and previous tests, including the data coming from point cloud clustering methods. According to this, we propose different families with low Level of Detail (LoD) and high Level of Information (LoI), including strategies for integrating the data of point cloud clustering methods. This proposal is applied to a case study in the Fortress of Almeida (Portugal), demonstrating the viability of the approach for the diagnosis of historical constructions. Future works will be focused on improving the integration of the 3D point clouds features by using convex-hull methods as well as integrating the results of clustering approaches based on artificial intelligence. • Different HBIM families are proposed for repressing damages and previous tests. • A low LoD and a high LoI are proposed for the diagnosis of historical buildings. • The use of path-type families with high LoI proves to be a very efficient strategy. • It is possible to integrate the data of point cloud clustering methods in HBIM. • We have used this method for evaluating the conservation status of a construction. [ABSTRACT FROM AUTHOR]

Modern smart systems such as those needed for Industry 4.0 integrate data from various sources and increasingly require that data be contextualized with domain knowledge. The integration and contextualization of data allows for the advanced reasoning needed to generate knowledge grounded in the data under consideration. In this paper, we propose an architecture for an ontology-supported multi-context reasoning system which inherently supports a number of desired system qualities including data transparency, system interactivity, and graceful aging. The architecture is inspired by the Presentation–Abstraction–Control architecture style, which is an interaction-based architecture. Our architecture uses a two level hierarchy with three agents and can incorporate and utilize multiple contexts. It is flexible, supporting an interface between data and users, highly interactive, and easily maintained. The evolution of data is isolated to a single component of the system and therefore does not cascade to several others. A domain of application can be easily determined by the use of archetypes and domain-specification components. Our architecture is demonstrated using a case study involving data from the city of San Francisco. [ABSTRACT FROM AUTHOR]

• A novel sliding/rotating overhangs is presented and evaluated for US residential buildings. • Both energy efficiency and environmental impacts of the dynamic overhangs are evaluated. • PV-integrated control strategies are considered to operate the dynamic overhangs. • The PV-integrated dynamic overhangs can result in net-zero building designs. The paper evaluates the benefits of integrating PV arrays with sliding and rotating overhangs to both generate electricity and reduce heating and cooling thermal loads for housing units in US apartment buildings. Optimal controls to operate the sliding-rotating overhangs are considered to minimize annual net energy demands with and without the integrated PV arrays. A series of sensitivity analyses is performed to assess the impact of design and operating conditions on the energy performance as well as the environmental impacts of the sliding-rotating overhangs. The analysis results clearly indicate that when integrated with PV modules, sliding overhangs can significantly reduce the energy demand for US housing units especially when they are set at the optimal angles specific to the building location to maximize their electricity generation and solar shading effects. Specifically, it is found that sliding overhangs can not only reduce energy demand but also can achieve net-zero energy conditions for US apartment units with large windows and located in mild climates even when monthly adjustments are applied to operate the shading systems. For instance, 9.2% of the total energy use of an apartment housing unit located in Boulder, CO, can be reduced using the sliding-rotating overhang with no-PV. In San Francisco, CA, more than 100% of the apartment housing unit energy demand can be avoided using PV-integrated sliding-rotating overhangs. To minimize design and operation complexities, it is recommended to consider sliding-only overhangs set at the latitude of the location to reduce both heating and cooling demand as well as increase PV array output. For instance, it is found that sliding overhang set at the latitude angle can be sufficient to achieve net-zero status and even a carbon-neutral operation for an apartment housing unit located in San Francisco, CA, even when adjusted on a monthly basis. [ABSTRACT FROM AUTHOR]

A novel dynamic overhang system is evaluated in this paper when applied to control solar heat gains through windows of US residential buildings. In particular, the energy performance of the dynamic overhangs is compared to static systems using various operation scenarios. The main objective for the control strategies evaluated in the analysis is to minimize annual heating and cooling energy consumption when dynamic instead of static overhangs are applied to US residential buildings. The operation of the dynamic overhangs is adjusted to account for the range of positions allowed as well as the setting frequencies. In particular, the dynamic overhangs can be set on hourly, daily, and monthly basis depending on the window orientation and the time of the year. The analysis has indicated that the performance of the dynamic overhangs depend on their design specifications as well as on the window glazing type. However, the energy saving potential for the dynamic overhangs compared to static devices depend largely on the window size and the climate with significant reduction in cooling energy use achieved in hot and mild US climatic zones. Indeed, the dynamic overhangs can save over 45% of the air conditioning needs for US homes with window-to-wall ratio of 30% located in Phoenix, AZ, and San Francisco, CA, when automatically operated on hourly basis. [ABSTRACT FROM AUTHOR]

This paper presents a rural exemplar house built in San Francisco de Raymina (a high Andean village 3700 masl) in southern Peru that integrates passive and sustainable solar heating techniques. A climatic analysis of this village was carried out using measurements of meteorological parameters recorded throughout a whole year. The annually averaged temperature, relative humidity and horizontal daily solar energy were 8.3 °C, 73.1% and 5.2 kWh/m2, respectively. The temperatures outside and inside the most rural dwellings are almost the same, so they do not offer any protection specially, during nights when the temperature can reach values below zero. The thermal behavior of the house was modeled with the m2m tool, and an experimental validation was carried out. With the use of m2m, it was possible to create an energy balance during the month of June 2014 (the winter cold and dry season) to determine the energy loss/gain contributions by each wall and to assess how air exchanges (the flow rates of which were deduced using an inversion approach, as they could not be directly measured) between the exterior and interior influence the thermal behavior of the whole house. Infiltration contributed approximately 48.6% of the daily energy losses, while the main solar gains were from the skylights (21.8%) and the adobe walls, which absorbed heat during the day and released heat at night. [ABSTRACT FROM AUTHOR]

Street categorization is an important topic in urban planning and in various applications such as routing and environment monitoring. Typically streets are classified as commercial, residential, and industrial. However, such broad categorization is insufficient to capture the rich properties a street may possess, and often cannot be used for specific applications. Previous works have proposed several advanced street categorization systems. However, most of these systems rely on manual analysis and design, which requires significant effort. In this paper, we propose a method for automatically discovering latent street types from multi-modal Web open data. We utilize data of different modalities including microblog tweets, Foursquare venues, and Google Street View images. The model we propose considers both coherence within each modality and association between modalities. Based on the San Francisco city data, our quantitative evaluation shows superiority of the proposed method in terms of coherence and association. In qualitative analysis, we show that the street types discovered by our method correspond to the official street plan. We also show an example application in which the discovered street types are used in crime prediction. • Automatically learning latent street types using three Web data sources. • The extended LDA model improves handling of multi-modal data. • Learned street types matches traditional street type designs. • The effect of learned street types is demonstrated on crime prediction. [ABSTRACT FROM AUTHOR]

• Performance can be significantly improved without stiffening the entire building. • Relative contribution of transient and permanent drift damage to the total is key. • For repair cost, stiffness increase is most effective regardless of building period. • For loss of function, strength increase is more effective for longer period buildings. • Increasing partition drift capacity is a viable alternative to stiffness and strength. Building seismic performance plays an important role in earthquake resilience. This role can be characterized beyond safety and collapse prevention, which are the primary building code objectives, by evaluating the loss of function and costs of repair of buildings. One important measure for building performance evaluation is the damage that occurs when structural and nonstructural elements are deformed beyond their capacity during an earthquake. Through a risk-based performance evaluation procedure, the expected impact of changes in stiffness, strength, and deformation capacity on building damage can be quantified and the results can be utilized to make targeted improvements to building codes to enhance earthquake resilience. In this paper, models of 4, 7, 10, and 15-story reinforced concrete special moment frame structures designed for San Francisco are subjected to suites of earthquake ground motion records to evaluate deformation response for a range of earthquake intensities. From these analyses the statistics of demand are estimated and used, along with models of capacity and seismic hazard, to evaluate the expected repair costs and loss of function over the life of each building. Performing this analysis for a range of values for each design variable will demonstrate the influence of stiffness, strength, and deformation capacity on building seismic performance, which can be used for evaluating and enhancing community resilience. [ABSTRACT FROM AUTHOR]