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Development of digital structural maintenance for federal highway engineering structures – Workshop "Digital structural maintenance including structural inspection" in Bonn in February 2024 This report presents the results of the workshop "Digital building maintenance including structural inspection", which took place in February 2024 in Bonn at the Federal Ministry for Digital and Transport. The aim of the event was to highlight market‐ready solutions related to digitalization and their future developments, as well as to formulate requirements for further developments by users. During individual workshop sessions, participants from industry and administration discussed the challenges, potentials, and next steps for implementation on the topics of structural inspection, non‐destructive testing, and monitoring. The results indicated that the field of building maintenance has significant potential for support through digital technologies. This includes, among other things, the adaptation of relevant regulations, the use of best practice applications, the integration of digital technologies into the maintenance process, support in the development of a data management system, and the training of relevant processes and workflows. [ABSTRACT FROM AUTHOR]

The operations and maintenance phase is typically the longest phase within the building life cycle. Proper and consistent building maintenance is imperative for several reasons, including extending the life of buildings, improving occupants' comfort and safety, and ultimately saving on long-term costs. Budgets for maintenance should be foreseen. The costs of statutory periodic inspections, the costs of replacing worn materials and elements, the costs of periodic works and repairs, the costs of reactive maintenance and the costs of operation will be shown for the analyzed building—a school. This paper outlines the development of a 15-year maintenance plan and program for the building, covering the period from 2024 to 2038. The plan incorporates a discount rate of 3.64% and accounts for inflation when calculating costs. The present value of the costs of maintenance and operation of the building for 15 years is EUR 1,978,292.20 and the largest share of these costs is the operation cost of the building with EUR 1,599,002.18 (80.83%). A sensitivity analysis was conducted by varying the discount rate and analysis period, resulting in recalculated present values for maintenance costs. The analysis reveals a correlation of 26.73% between the present value of maintenance and operation costs over a 15-year period and the associated capital costs. [ABSTRACT FROM AUTHOR]

The rapid booming of information and communication technologies (ICT) and artificial intelligence has promoted the development of maintenance practices in the building industry towards a data-driven paradigm, of which the digital twin (DT) serves as the fundamental framework to strengthen data transit ability and interoperability. Among the state-of-the-art approaches in the maintenance industry, predictive maintenance (PdM) is a prominent approach by anticipating unexpected failures and unscheduled maintenance tasks. However, most current PdM frameworks are facility-specific, lacking generality and scalability. Besides, existing solutions mainly concentrate on condition monitoring and fault identification rather than failure prediction. Moreover, good prediction results rely heavily on sufficient labelled data sets, which are costly and labour-intensive to collect. To address these issues, the author developed a unified PdM framework for the building industry from the DT perspective. Next, a novel failure prediction method utilising the Semi-supervised Generative Adversarial Network (GAN) has been proposed in this article, which makes effective utilisation of both labelled and unlabelled data. Finally, an online platform has been developed to present the monitoring and prediction information. Experimental findings show the effectiveness and superiority of the proposed method for failure prediction through public data sets of building facilities. [ABSTRACT FROM AUTHOR]

Purpose: The paper aims to identify the critical constraints that impede heritage building (HB) facility managers from discharging their duties effectively and develop an index model to guide HB maintenance management (HBMM) practitioners to the critical constraints. Design/methodology/approach: A literature review was conducted to identify HBMM constraints. Facilty management practitioners assessed the constraints' significance through an online survey. The factor analysis was used to shortlist and group the constraints, and the constraint clusters were analyzed by the fuzzy synthetic evaluation technique. A significant index cluster to determine HBMM constraints criticality was generated using the linear additive model. Findings: Embracing a total of 16 HBMM constraints, the three clusters identified are: (1) managerial and inadequacy constraints, (2) pressure and bureaucracy constraints and (3) HB peculiarities constraints. Based on the generated significant index, the HB peculiarities cluster was identified as the most significant. Research limitations/implications: The study was conducted in a particular jurisdiction, limiting the generalizability of the result. Future research should address this limitation by covering more jurisdictions. Practical implications: The significant index model (SIM) developed enables HBMM practitioners to objectively assess the criticality of HB constraints and facilitates them to effectively strategize and allocate resources for HBMM. Originality/value: The SIM, which transforms subjective judgment into the objective assessment of the HBMM constraints' criticality, can assist practitioners, policymakers and other HBMM stakeholders in implementing strategies for the sustainability of HBs. [ABSTRACT FROM AUTHOR]

In response to problems such as insufficient adhesion, difficulty in adjustment, and weak obstacle-crossing capabilities in traditional robots, an innovative design has been developed for a five-wheeled climbing robot equipped with a pendulum-style magnetic control adsorption module. This design effectively reduces the weight of the robot, and sensors on the magnetic adsorption module enable real-time monitoring of magnetic force. Intelligent control adjusts the pendulum angle to modify the magnetic force according to different wall conditions. The magnetic adsorption module, using a Halbach array, enhances the concentration effect of the magnetic field, ensuring excellent performance in high-load tasks such as building maintenance, bridge inspection, and ship cleaning. The five-wheel structural design enhances the stability and obstacle-crossing capability, making it suitable for all-terrain environments. Simulation experiments using Maxwell analyzed the effects of the magnetic gap and the angle between the adsorption module and the wall, and mechanical performance analysis confirmed the robot's ability to adhere safely and operate stably. [ABSTRACT FROM AUTHOR]

This perspective article describes the past, present, and future directions on TiO2 photocatalysis, 3D concrete printing, and recycling for developing innovative building envelope components from façade skins. Using a methodology consisting of three phases, first the historical evolution of TiO2 photocatalysis, 3D concrete printing, and recycling policies was investigated. Second, the rationale and positioning with regard to the state of the art were developed. Third, the cementitious mix design assessment for 3D printing of the building envelope components, evaluation of the photocatalytic activity, evaluation of the self-cleaning and air-purifying properties, and environmental and health assessment of the building envelope components were identified and discussed as major issues in developing innovative building envelope components that have the potential to mitigate urban air pollution, reduce building maintenance activities, and reduce building embedded carbon while, for example, recycling significant amounts of construction and demolition waste. [ABSTRACT FROM AUTHOR]

Proper design and operation of buildings are expected to result in optimal thermal comfort and energy performance at the same time. If occupants are not satisfied with thermal conditions, corrective actions by building managers and maintenance staff may lead to elevated room temperatures with evident energy penalties. Because of complicated technical systems and control logic, it is worth studying how well the design intent has been realised in new office buildings. In this study, thermal comfort was analysed by measurements of draught, room, and supply air temperature as well as with occupant questionnaire surveys in five modern office buildings. Both short‐ and long‐term measurements were conducted to demonstrate problems in the operation and to find potential solutions for improvement. The results revealed an issue of excessive overheating during the heating season despite generally low air velocities. Radiant ceiling panels had the lowest velocities in both summer and winter, while buildings with active chilled beams showed the potential to meet Category II air velocity and temperature requirements. The building with thermally activated building systems experienced the most overheating during the heating season. Occupants were satisfied with the heating season temperatures of 23°C–25°C that can be attributed to lighter clothing (0.7 clo) instead of the standard 1.0 clo. Ventilation supply air and indoor temperature analyses indicate that elevated setpoints have been used to compensate for draught, resulting in overheating. As a measure of improvement to avoid overheating, we propose control curves for room temperature based on the outdoor running mean temperature and for supply air temperature based on the extract air temperature. [ABSTRACT FROM AUTHOR]

Addressing the difficult problem of predicting the height of water-conducting fracture zones in shallow and thin coal seams, a prediction model of water-conduction fracture zones based on a backpropagation (BP) neural network was developed by integrating theoretical analysis, field measurements, and algorithmic advancements. Firstly, through overburden migration analysis and correlation tests, the height index system of the water-conducting fracture zone was determined. This system includes mining height, buried depth, dip angle, working face width, and overburden rock lithology, with five groups of characteristic parameters. Then, 35 pairs of minefield-measured data were collected to establish the measured height data set of the water-conducting fracture zone. Secondly, a BP neural network prediction model and a traditional support vector regression (SVR) prediction model were constructed based on a Pytorch framework, and the models were trained and tested by selecting data sets. Thirdly, the optimal prediction model was determined by comparing the model with the empirical model and multiple regression model of mining regulations for coal pillar maintenance and pressure in buildings, water bodies, railways, and main shafts. Finally, a typical mine was selected for application to verify the suitability of the optimal model. The results show that: (1) the predicted value of the neural network model is consistent with the change trend of the measured value, which accords with the theoretical law; (2) compared with traditional forecasting methods, the error of the BP neural network prediction model is stable and the prediction effect is the best; (3) dropout can effectively mitigate mitigation training overfitting, achieve regularization, and improve prediction accuracy; (4) the field application further verified that the BP neural network model is the best for predicting the height of water-conducting fracture zones of extremely thin coal seams, and the research results can provide technical guidance for similar fragile coal seams. [ABSTRACT FROM AUTHOR]

Featured Application: This article applies to building maintenance and the life-cycle assessment of repair operations. The possibility of diagnosing and repairing specific sets of defects (those with more severe consequences and impact on the degradation of building components) with the same means is considered. It is important to optimise the planning and kick-off of building inspections by using, from the start, a predetermined set of equipment and to optimise the maintenance and rehabilitation of the building envelope in terms of funds invested and resources. An existing methodology is used to create inter-defect correlation matrices, taking into account an expert knowledge-based building inspection system. The main results include a set of essential diagnosis methods—crack measuring and monitoring; temperature and moisture measurement; infrared thermography; and water absorption tests—and the identification of the most transversal repair techniques—cleaning; protection coating; replacement/reapplication of claddings/glazing; re-application of finishing coats or more adequate claddings—which were also analysed in terms of resource consumption, as a preliminary approach to their life-cycle assessment. The main conclusions indicate that there is still a long path to cover in the field of life-cycle assessment of repair techniques, which can be extended to the application of diagnosis methods. [ABSTRACT FROM AUTHOR]

The architectural heritage of a particular place, in most cases, is characterised by vernacular and unique constructions that have been adapted to local climate conditions. For this purpose, specific materials and construction methods have traditionally been used that, in addition to the durability of the construction, also allow for the consideration of the energy efficiency of the building itself. The present intersection of climate change and architecture has led to new exposure to the external agents for which constructions were designed, forcing, in most cases, a review of building envelopes and very costly proposals. From the point of view of efficiency, intervention strategies with passive measures are proposed that not only improve the energy performance and maintenance of buildings themselves, but also lower the overall energy consumption. Using a heritage case study of the city of Seville, the Moroccan Pavilion, at Expo 92, this work includes an analysis and proposal of effective action through a methodological study of energy efficiency. The problem of high energy consumption during the summer months in Seville is tested in the Pavilion. The results indicate an urgent need for renovation, and among different options, new intervention measures are recommended as an alternative to consumption based on knowledge and tradition; moreover, passive construction elements are proposed in accordance with the climatic reality of the environment for optimal conservation in new climate scenarios. [ABSTRACT FROM AUTHOR]

BIM‐based assessment of sustainable maintenance measures for existing building structures In Germany, around 70 % of all building construction work is done on existing buildings. This high proportion emphasizes the need for the maintenance management of buildings. For this purpose, digital methods and technologies are combined with integrating sustainability aspects for optimized decision‐making. In this paper, a holistic method for the building maintenance is developed by using the BIM‐method in combination with the integration of sustainability aspects. Different digital technologies are combined within a semi‐automated workflow. In the first step, the status quo of digital maintenance on reinforced concrete structures is analyzed in a literature review. Subsequently, the process‐related and data‐technical basics are defined. Based on this, the concept development and validation are carried out by holding the steps ‐ data acquisition, an algorithmic data process and sustainability analysis as well as data provision via augmented reality and a common data environment. At the end, the process steps are validated using a real demonstrator. The result of the article demonstrates a semi‐automated process chain and decision support for resource‐efficient maintenance management of reinforced concrete structures. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a developed BIMxAR application, an integration of building information modeling (BIM) with augmented reality (AR) linked with real-time online database to support the building facility management work. The primary aim of this research was to develop and empirically examine the applicability of a BIM-based AR (BIMxAR) application in building facility management. Design/methodology/approach: The BIMxAR application was developed and experimented with maintenance work of a university laboratory building. The experiment consisted of a comparison of supportive maintenance tasks performed using the traditional approach and the BIMxAR approach by 38 university students. The time taken to complete each task was recorded and analyzed using statistical analyses to compare the performance between the tasks completed using each approach. Findings: The results indicated that the group using the BIMxAR application approach completed the tasks correctly in a significantly shorter time compared to that using the traditional approach. The findings supported the applicability of the developed BIMxAR application and the improvement of the building facility management tasks when using the proposed approach. Originality/value: This paper presents a methodological approach in developing a mobile application that integrates BIM with AR for facility management work, leveraging real-time information exchange through a cloud-based platform. The paper also provides empirical evidence that demonstrates how the integration between BIM and AR could be achieved and implemented to help facilitate building maintenance tasks. [ABSTRACT FROM AUTHOR]

Pacenan house is a traditional house of the coastal area in Situbondo. The architectural style of the Pacenan house is adapted from the Tanean Panjang traditional house. The spatial pattern of the Pacenan house differs from that of Tanean Lanjhang, as the Pacenan house only has "roma" (rooms) and "amper" (open spaces). However, over time, the spatial patterns of the Pacenan house have transformed. Therefore, this study was conducted to identify the transformations in spatial patterns and their impact on the building shapes of Pacenan houses in Sumberejo, Situbondo. Research data was obtained through field observations and interviews. The data was then analyzed descriptively. The results indicate changes in spatial patterns occurring in Pacenan houses in the village of Sumberejo, including the addition of rooms and alterations in the function of spaces. Transformations in spatial patterns are influenced by space requirements, land availability, social factors, and economic considerations. The impact of these changes in spatial patterns varies; some result in alterations to the building's form, while others do not transform the structure. [ABSTRACT FROM AUTHOR]

Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area in damage images. In step one, a region-based convolutional neural network (YOLOv8) is applied to train the crack localizing model. In step two, Gaussian filtering, Canny, and FindContours are integrated to extract the reference contour (a pre-designed seal) to obtain the conversion scale between pixels and millimeter-wise sizes. In step three, the recognized crack bounding box is cropped, and the ApproxPolyDP function and Hough transform are performed to quantify crack dimensions based on the conversion ratio. The developed framework was validated on a dataset of 4630 crack images, and the model training took 150 epochs. Results show that the average crack detection accuracy reaches 95.7%, and the precision of quantified dimensions is over 90%, while the error increases as the crack size grows smaller (increasing to 8% when the crack width is within 1 mm). The proposed method can help engineers to efficiently achieve crack information at building inspection sites, while the reference frame must be pre-marked near the crack, which may limit the scope of application scenarios. In addition, the robustness and accuracy of the developed image processing techniques-based crack quantification algorithm need to be further improved to meet the requirements in real cases when the crack is located within a complex background. [ABSTRACT FROM AUTHOR]

Heritage buildings are crucial for any area's cultural and political aspects. Proper maintenance and monitoring are essential for the conservation of these buildings. However, manual inspections are time‐consuming and expensive. We propose a deep learning–based detection framework to identify the damages on the ancient architectural wall. The algorithm applied in this study is YOLOv5. Comparing its five different versions, it was decided to use YOLOv5m as the most accurate detection algorithm with a mAP of 0.801. The damage types identified are physical weathering and visitors' scratches. High‐resolution images were selected for the experiment and effectively identified image. In addition, the applied algorithm allows real‐time detection and the identification of seasonal sources of disruption, which is proved by the video test in this study. The findings contribute to the development of an intelligent tool for health monitoring with the goal of fast and remote damage detection in the routine maintenance of heritage buildings. [ABSTRACT FROM AUTHOR]

Liveable housing environments face the menace of global climate change. Built infrastructure (including buildings and houses) continuously experiences significant impacts that are exacerbated by natural variability in the climate. Our study examined how climate change impacts the resilience of residential buildings, increases maintenance frequency, and the wellbeing and comfort of residents in UK residential buildings. This study used deductive reasoning and an empirical epistemological methodology as the basis of primary data collection via a questionnaire survey. The instrument was designed to gather data on the frequency of maintenance and the wellbeing of residents and their perceptions regarding the impacts of climate change. Through regression analysis of the data, the findings showed a significant relationship between climate change and the wellbeing of the occupants of UK residential buildings. Also, physical wellbeing and social wellbeing are more important to the occupants than their mental wellbeing. The cost of maintenance of residential buildings in the UK has an upward trajectory due to the continuously reducing resilience of building fabrics caused by the impacts of climate change; for instance, a recent increase in rainfall/storms resulted in unprecedented flooding, which damaged the fabrics of some UK residential buildings. [ABSTRACT FROM AUTHOR]

The available information on endotoxin in Chinese households is limited and there is inconsistency regarding its impact on asthma and allergies in children. A case-control study was performed in 324 homes in Tianjin, China. Linear regression analysis was performed to identify the determinants of endotoxin concentrations in household dust. Logistic regression models were employed to investigate the associations of endotoxin concentrations with asthma and allergies in children. Endotoxin concentrations were determined from 284 valid dust samples, ranging from 94 to 11,625 EU/g, with a mean concentration of 3638 EU/g. We found a significant positive association between endotoxin concentrations and children's current asthma. Old houses, ventilation systems without exhaust fans and windows opened infrequently were related to higher concentrations of endotoxins. In conclusion, endotoxin exposure in the home might be a risk factor for current asthma in children. Strategies for controlling endotoxin concentrations such as building maintenance and ventilation improvements are recommended. [ABSTRACT FROM AUTHOR]

The benefits of circularity and biogenic carbon storage are often overseen. This study links the circular design of buildings with prolonged biogenic carbon storage. Circularity in architectural design can involve extending the service life of a building frame, whilst forests grow back and store more carbon. Following this approach, Stora Enso has developed a mixed-use building concept with flexible and adaptable structures. Static and dynamic life cycle assessment (LCA) has been carried out to assess different scenarios, modelling and quantifying its potential benefits regarding whole life carbon. While whole life carbon is lower in all timber scenarios compared to conventional concrete buildings, dynamic LCA makes clear the benefits of carbon storage and carbon sequestration. Total emissions, considering a reference service life of 50 years, are 2,84 kg CO2-eq./m² floor area/year, considering biogenic carbon storage and carbon sequestration in regrowing forests. An increase of the building lifetime to 80 years aligns with a longer rotation time of forest trees, resulting in whole life carbon of -0,09 kg CO2-eq./m² floor area/year. This demonstrates that the effective implementation of built-in flexibility and adaptability can extend the service life of a building, unlocking environmental benefits of biogenic carbon storage of wood products in buildings. [ABSTRACT FROM AUTHOR]

The effect of energy saving and carbon reduction in the building industry is closely related to the realization of China's double carbon goal. In this study, a two-dimensional framework for building life cycle carbon emissions was established, which takes into account the early stages of building design such as the feasibility study stage and scheme design stage. Taking 57 residential buildings in Xi'an City as examples, the life cycle carbon emission characteristics of residential buildings in cold areas were introduced. This study found that the life cycle carbon emission intensity is about 45~55 kgCO2/(m2·a). The operation and maintenance stage and building materials production stage accounted for the largest proportion of carbon emissions, and the sum of carbon emissions of the two stages accounted for 92.3% of the total carbon emissions. In addition, based on the probability density function, the carbon emission intensity distributions of the building life cycle, building material production stage, building material transportation stage, and operation and maintenance stage were analyzed, and it was found that each distribution fitting graph was generally in line with a lognormal distribution, and their expected value provided a reference index for carbon emission pre-assessment in the feasibility study stage. Based on the analysis and determination of 11 independent variables that affect the total carbon emissions, such as area, floor number, story height, and number of households, a multiple linear regression model for carbon emission pre-assessment in the design stage of building schemes is proposed. The R2adj of the model is 0.985 and the error is about 10%. The prediction model can provide beneficial guidance for the life cycle carbon emission prediction in the design stage of building projects, so as to reduce carbon emissions by changing building design. [ABSTRACT FROM AUTHOR]

The public buildings experience poor maintenance, and this is sometimes attributed to neglect and lack of funds, and this results in a decline, user dissatisfaction, and a cycle of neglect again. The purpose of this research is to evaluate the maintenance practices of some selected public buildings in the Central Region of Ghana. A mix of stratified and random sampling techniques was used to select 80 (maintenance personnel and users) respondents from the Development Offices of the University of Cape Coast (UCC) and Cape Coast Technical University (CCTU) in Ghana. The questionnaires were administered online, and quantitative data were analyzed using Statistical Package for Social Sciences (SPSS) version 26 and Microsoft Excel with descriptive statistics and correlations. The study results revealed that maintenance-related cultural factors were appreciated most as compared to other factors like teamwork and collaboration. Increased community participation may also be brought about through campaigns and partnerships. New technologies for building health monitoring were considered as appropriate investments, while funds for training the maintenance personnel were deemed necessary. The study reveals gaps in bureaucracy, planning, and roles and responsibilities in Ghana's maintenance culture. It calls for a comprehensive strategy to improve this culture, including funding, reducing red tape, embracing technology, and promoting public awareness and staff development. Clear roles and responsibilities, along with performance indicators, are also crucial. The study, targeting the Central Region, helps understand regional disparities in challenges and opportunities. It uses a mixedmethods approach and considers individual and organizational levels to understand maintenance activities in a developing environment. Although the sample size may limit generalization, the study provides valuable information for policymakers and facility managers aiming to prolong the lifespan and minimize maintenance costs of public buildings in Ghana. [ABSTRACT FROM AUTHOR]

In the inner areas of large cities, many residential buildings built at the turn of the 19th and 20th centuries remain standing. The maintenance and renovation of these buildings have emerged as critical priorities over recent decades. E.g., in Budapest during the socialist era, the majority of these buildings were not renovated, and maintenance was largely neglected. In the subsequent 10–15 years following the end of socialism, financial resources for renovations were scarce due to the extensive transfer of properties from state to private ownership. It is only in the last decade or so that renovations have begun to be systematically addressed. Consequently, a significant portion of the building stock is still pending renovation. Given the current economic conditions, sustainable maintenance and necessary conversion are of paramount importance. Unfortunately, few standardized condition assessment methods are implemented in industrial practice, and the literature on this topic is limited. To address these challenges, we have developed an algorithm and model for condition assessment and decision support, which we refer to as the Complex Building's Decision Support System based on Fuzzy Signatures (CBDF system). Our model employs a fuzzy signature-based approach to account for uncertainties, errors, and potentially missing data that may arise during the assessment process. The primary aim of this model is to equip professionals involved in building condition assessment with a tool that enables them to make consistent and objective decisions while minimizing errors. This paper provides a brief overview of the CBDF system and presents test results from the assessment of a selected structural component of a building, demonstrating the system's functionality. [ABSTRACT FROM AUTHOR]

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In this article, comprehensive insights into the field of building maintenance, emphasizing the importance of keywords, collaborative efforts among authors, and the evolving research landscape, are provided. The use stage, as the longest phase in a building's life cycle, involves economic, technical, and social activities. Numerous authors have contributed to the broader topic of building maintenance, covering aspects such as maintenance prioritization, improvement actions, decision-making guidelines, factors affecting success, cost estimation models, and BIM usage in maintenance. The study conducts a thorough literature review using Scopus as a search engine, employing the full-counting method for authorship, and uses VOSviewer 1.6.20 software for bibliometric analysis to identify gaps and outline future research directions. The results reveal distinct data sets focusing on building maintenance from general to specific, with each set reflecting unique aspects and considerations. Bibliometric maps illustrate author collaboration and keyword connections within each set, highlighting evolving research trends and areas of focus. Overall, the analysis highlights the importance of prioritizing maintenance, integrating technology, and fostering collaboration to enhance building maintenance in educational institutions. The study concludes with recommendations for future research, emphasizing the ongoing need for exploration and development in building maintenance, especially on maintenance strategies and Life Cycle Costs of university buildings. [ABSTRACT FROM AUTHOR]

Constructing Bayesian networks (BN) for practical applications presents significant challenges, especially in domains with limited empirical data available. In such situations, field experts are often consulted to estimate the model's parameters, for instance, rank correlations in Gaussian copula-based Bayesian networks (GCBN). Because there is no consensus on a 'best' approach for eliciting these correlations, this paper proposes a framework that uses probabilities of concordance for assessing dependence, and the dependence calibration score to aggregate experts' judgments. To demonstrate the relevance of our approach, the latter is implemented to populate a GCBN intended to estimate the condition of air handling units' components—a key challenge in building asset management. While the elicitation of concordance probabilities was well received by the questionnaire respondents, the analysis of the results reveals notable disparities in the experts' ability to quantify uncertainty. Moreover, the application of the dependence calibration aggregation method was hindered by the absence of relevant seed variables, thus failing to evaluate the participants' field expertise. All in all, while the authors do not recommend to use the current model in practice, this study suggests that concordance probabilities should be further explored as an alternative approach for the elicitation of dependence. [ABSTRACT FROM AUTHOR]

Successful adoption of the principles for establishing building maintenance procedures still remains partial, particularly in developing countries. Well-known obstacles, such as a lack of methodology and planning, knowledge of staff and quality required, and budget limitations, may all outweigh any practical information and guides to efficiently achieving maintenance objectives for existing buildings. Given that, in Iraq, public office buildings are one of the largest parts of the buildings construction sector, and that Iraq is considered to have one of the largest rates of maintenance work expenditure, significant investigation is needed. However, research related to Iraqi maintenance operations is limited to a few, general aspect overviews of some elements of maintenance. These require amalgamation to overcome all maintenance issues and establish a future strategy for efficient maintenance operations. This paper presents a detailed review and quantitative investigation of the barriers, success factors, and key drivers regarding effective building maintenance practice in Iraq. Based on feedback from 72 specialist stakeholder respondents and interviews with experts at different levels, it can be said that building maintenance procedures in this study area are still in their infancy. In order to reinforce building maintenance as a robust and unified practice, the goal should be to formulate a comprehensive plan earlier in the building process, based on the drivers that have been identified and on the deep knowledge stakeholders possess about successful building maintenance procedures. The study concludes that budgets for building maintenance operations should be aligned with expected building deficiencies during their operating phase and with building standards quality, and that the failure of such estimations can render maintenance operations inefficient. Thus, the findings can provide a best practice guide to aid future researchers in managing maintenance operations within a framework based on investigated factors that guarantees long-term benefits. [ABSTRACT FROM AUTHOR]

This study examines the strategic use of life cycle cost analyses (LCCAs) in the management and conservation of heritage sites, emphasizing the need for comprehensive financial planning. With an increasing number of heritage sites showing signs of deterioration, our aim was to improve the sustainability and effectiveness of restoration practices. We used dynamic life cycle costing methods and developed the MONUREV software V2 to simulate different restoration scenarios, providing accurate, data-driven projections for maintaining structural, functional and aesthetic integrity. The field research involved testing these methods through case studies of heritage buildings in the Czech Republic, focusing on holistic cost management from initial analysis to practical application. The results showed that LCC analysis can significantly assist in making informed decisions, balancing economic and cultural values, and ensuring long-term conservation outcomes. This study concludes that the integration of a detailed LCC analysis into heritage conservation strategies represents a methodological advance that can significantly improve the economic and operational planning of the maintenance of heritage buildings, thereby ensuring their preservation for future generations. [ABSTRACT FROM AUTHOR]

Building defects are very prevalent and contribute significantly to a building's economic value. There are numerous information sources on building defects that have significant potential for learning more about building defects. This study aimed to identify the information sources used in previous building defect studies and to identify the motivation for carrying out such studies. To fulfil this aim, a scoping study was carried out. The information sources identified included insurance companies, private databases, questionnaire surveys, lawsuits, building surveys, client complaint forms, and maintenance reports. This study found that insurance company and client complaint forms include the largest collection of real building defect cases, but such databases may lack detailed descriptions of the causes of the defects. The main purposes of the building defects studies identified here included design challenges, identification of defects, building maintenance management, quality management, systematization in data collection, providing an overview of typical defects, and classifying defects. Identification was found to be the most common purpose, indicating that the industry wants to learn more. This study identified research gaps in the climate perspective in relation to building defects. Most of the studies focused on the economical perspective, and none focused on the carbon footprint perspective. This study provides an overview of information sources, which can motivate stakeholders to collaborate in learning about the physical causes of building defects as well as about other key aspects such as fault, finances consequences, and procedural causes. [ABSTRACT FROM AUTHOR]

Purpose: Green building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London. Design/methodology/approach: This study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)). Findings: GB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework. Originality/value: Industry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully. [ABSTRACT FROM AUTHOR]

Building maintenance is a pivotal determinant for ensuring uninterrupted and flawless operations for facilities. From the moment construction is completed until the demolition, maintenance activities must be diligently performed to establish a secure working environment. Building maintenance costs hold the considerable cost for an organization. Consequently, the effective execution of building maintenance procedures becomes imperative to reduce maintenance expenses and enhance workplace safety. Maintenance errors arise due to a range of factors, including human, management, building design, and machinery errors. Among these causative factors, human errors were more likely to occur. Accordingly, this study aims to identify the causal relationships of human errors in building maintenance. Mixed-method research approach was used with a survey strategy, where primary data was collected from a questionnaire survey and subsequent expert interviews. Data were analyzed under both qualitative and quantitative analytical approaches. 39 causes of human errors were identified through literature and among which, 13 most critical causes of human errors were identified. These critical causes were classified into four category groups, viz. information and communication, resource, management, and design, construction, and installation related. Critical human error probabilities were computed and used to develop a Bayesian Belief Network (BBN) to describe the causal relationships. [ABSTRACT FROM AUTHOR]

Building Information Modelling (BIM) methodology has been supporting the construction activity in all sectors involved: multidisciplinary designs development; construction planning and monitoring; building management and maintenance. A BIM environment aggregates several specialties and related professionals and in order to control and improve the quality of a BIM project, a BIM manager is required. The BIM manager has the responsibility to coordinate all tasks involved in a building design and in the associated activities that are usually workout over the project documents. A BIM platform can be considered as a useful resource for multidisciplinary coordination, extending the BIM 3D modelling scope into the construction planning and after, in the operation and maintenance stages. A BIM manager is then required: to incentive the collaboration at work and to control responsibility given to each professional involved; to guaranty the correction of the amount and quality of the information, included in each BIM model step, and transferred between tasks; to coordinate all designs and the distinct mandatory activities (conflict detection, cost estimation or energetic simulation). The topic of the present study is illustrated with three building cases were distinct specific projects, disciplines and tasks were developed: structural analyses and reinforcement details; quantity take-off of materials and cost estimation; construction scheduling and simulation. The study presents the relevance of a correct coordination within the development of building projects. Although there are limitations in the implementation of BIM methodology in all sectors or activities within the construction industry, BIM has been bringing an important improvement in the quality of a building design, reflected in the quality of the final product. BIM methodology is a current demand in the construction industry supported on advanced technology and in an adequate management of projects. [ABSTRACT FROM AUTHOR]

Concerning preventive maintenance plans for heritage timber buildings, computational methods are pioneering knowledge for the implementation of new preservation approaches in heritage structure management. In this context, fuzzy logic and random forest methodologies manage both data obtained from professional experts and data obtained in situ from the buildings themselves. This kind of digital procedure can harmonize the outcomes of building assessments because slight variations in the evaluation of input parameters do not produce a significant dispersion over the model's output. Preventive conservation strategies require cooperation among qualified experts who examine multidisciplinary knowledge related to heritage properties. Thus, new digital protocols and procedures that help decision makers prioritize preventive interventions and avoid corrective actions are paramount in minimizing the irreparable loss of properties. The main aim of this research is a new approach to two computational management systems: fuzzy logic and random forest. The outcomes of this study will be useful to stakeholders who are responsible for the maintenance of heritage buildings, as this methodology reduces the probability of failure and uncertainty during decision-making. The instruments derived will establish mitigation strategies oriented toward proactive future maintenance programs for heritage timber buildings in southern Chile. [ABSTRACT FROM AUTHOR]

Purpose: Social housing offers occupants comfort, safety and protection against extreme weather conditions. However, social housing occupants in various regions of Nigeria face various challenges. This paper aims to use a quantitative approach to examine the factors that contribute to the challenges faced by occupants in social housing within a semi-arid climate of Nigeria. Design/methodology/approach: An exploratory cross-sectional survey was used to administer 1,032 copies of structured questionnaires to occupants of social housing in Maiduguri, one of the largest urban centers of the semi-arid climate in Nigeria. A total of 955 responses were retrieved, giving a response rate of 92.5%. The statistical model used in analyzing data was relative importance indices and factor analysis. Findings: The results show that poor quality of FM services provided, poor maintenance of building components, damage to materials and valuables in the Harmattan period and housekeeping problems due to dust deposition in doors, while the poor response to occupants' complaints/reports is the least among the occupants' challenges in order of importance. Moreover, the results of the factor analysis further established that inadequate FM services and poor environmental conditions are the two factors contributing to the occupants' challenges in social housing within the semi-arid climate of Nigeria. Originality/value: This paper seeks to examine the factors contributing to social housing occupants' challenges in the semi-arid climate of Nigeria. The paper should inform policymakers, academics and professionals. [ABSTRACT FROM AUTHOR]

This research addresses the challenges faced in collecting and managing repair history information for architectural heritage due to its distribution among different institutions. To overcome this problem, this study proposes a building information modeling (BIM)-based repair history management framework that links non-formal repair details of architectural heritage to BIM. This framework was applied to the case study of the Great Hero Hall of Sudeoksa Temple in Yesan, South Korea. A repair history database in the form of structured data adhering to the historic building information modeling standard was utilized. The proposed framework effectively syncs the repair history database with BIM to preserve architectural heritage and streamline repair history information updates as well as management. Integrated heritage BIM is anticipated to furnish a thorough knowledge base for architectural heritage, comprising non-geometric details and formal information models, thus enhancing building maintenance planning. Ultimately, the benefits and limitations of the suggested framework and methodologies are deliberated for further implementation. [ABSTRACT FROM AUTHOR]

This study aims to determine the effectiveness and contribution of Tax Customs acquisition of land and building rights (BPHTB) and Urban and rural land and Building Tax (PBB-P2) to the receipt of local revenue (PAD) from 2018-2022. This research method uses a qualitative method that uses a descriptive approach. The object of this study is the Office of the Financial Management Agency of South Aceh region, the source of data used is secondary and primary data, secondary data obtained from the realization of the effectiveness and contribution of tax revenues acquisition of land and building duty (BPHTB) and land tax and Urban and rural building (PBB-P2), and primary data obtained from interviews. Based on the results of the discussion, the level of effectiveness of BPHTB from 2018-2022 is very effective with a percentage level of 167.45%, while the level of contribution is in the good category with a percentage of 45%. For the level of effectiveness of PBB-P2 from 2018-2022, it is quite effective with a value of 80.36%. While the level of contribution is in the medium category with a percentage value of 23%. [ABSTRACT FROM AUTHOR]

The majority of Europe's building stock consists of facilities built before 2001, presenting a substantial opportunity for energy efficiency improvements during their operation and maintenance phase. Digitalizing these buildings with digital twin technology can significantly enhance their energy efficiency. Reviewing the applications and trends of digital twins in this context is beneficial to understand the current state of the art and the specific challenges encountered when applying this technology to older buildings. This study focuses on the application of digital twins in building operations and maintenance (O & M), emphasizing energy efficiency throughout the building lifetime. A systematic process to select 21 pertinent use-case studies was performed, complemented by an analysis of six enterprise-level digital twin solutions. This was followed by an overview of general characteristics, thematic classification, detailed individual study analyses, and a comparison of digital twin solutions with commercial tools. Five main applications of digital twins were identified and examined: component monitoring, anomaly detection, operational optimization, predictive maintenance and simulation of alternative scenarios. The paper highlights challenges like the reliance on Building Information Modeling (BIM) and the need for robust data acquisition systems. These limitations hinder the implementation of digital twins, in particular in existing buildings with no digital information available. It concludes with future research directions emphasizing the development of methods not solely reliant on BIM data, integration challenges, and potential enhancements through AI and machine learning applications. [ABSTRACT FROM AUTHOR]

Equipment costs play a critical role in decision making during design and construction, which requires up-to-date information and data. The design of this study incorporates the inputs from the literature review on the influencing factors of equipment costs and major targeted equipment types to enhance decision support for equipment selection, project construction, and maintenance issues. Two traditional cost estimation methods and five machine-learning methods were compared in this study to identify significant attributes related to the predictions of the costs and residual values of each targeted equipment type. The novelty of this study is that the developed method improves prediction accuracy by establishing a comprehensive and well-structured database framework. A comparison of this method with the existing prediction models reveals that the results and the accuracy of multiple regression analysis are improved in the range of (3% to 33.97%) with the use of a modified decision-tree model combined with support vector machines. The major contribution of this research is the design, implementation, and validation of a machine-learning-based modified decision tree with a support vector machine model for improved accuracy and decision support in construction management. Future research should consider the relationship between geographical variations and value changes. [ABSTRACT FROM AUTHOR]