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The study aims to investigate the nomenclature of structural versus non-structural building adaptation measures taken by occupants of residential properties located in flood-prone areas of Port Harcourt, Nigeria, to verify whether they are adapted or maladapted relative to their level of flood exposure and underscore the role of disaster resilience in property management.

Using the survey research strategy, the uptake of structural and non-structural adaptation measures in spatially distributed areas of Port Harcourt was descriptively analyzed and tested to identify significant variation in the uptake of adaptation measures relative to increasing flooding levels, using a one-way ANOVA.

The study outcome reveals that there are some distinctive features of buildings in Port Harcourt which provide wet-proofing structural adaptation qualities while dry-proofing structural adaptation measures are not adequate. However, although some key structural adaptation measures were adapted relative to the levels of flood exposure, most structural features of the buildings do not vary relative to flooding levels, indicating maladaptation even with increasing flood risk. The uptake of non-structural measures is also inadequate and maladapted relative to changes in the level of flood exposure, despite their non-intrusive and low-cost nature. Overall, the findings imply that maladaptation is more prevalent.

The study provides a descriptive snapshot of the nomenclature of property-level flood risk adaptation in residential buildings, while highlighting the maladaptive tendencies/behavior of property occupants, within a metropolitan setting in the developing world, necessary to inform policy and provide sensitization for fostering disaster resilience in property management.

China is a large country with different regions due to regional differences and project characteristics, and the selection of prefabricated building technology according to local conditions is the key to its sustainable development in China. The purpose of this paper is to develop the suitability evaluation system of prefabricated building technology from the perspective of the suitability concept and to analyze the selection path of prefabricated building technology and to provide a reference for selecting and developing prefabricated building technology schemes that meet regional endowments.

Based on relevant literature, technical specifications, and standards, this paper constructs an index system for analyzing the technical suitability of prefabricated buildings. It includes 23 indicators, 7 dimensions, and 3 aspects through the semantic clustering method. Following this, the comprehensive weight of each index is determined using the order relation method (G1) and the continuous ordered weighted averaging (COWA). The selection of technical schemes is comprehensively evaluated using Visekriterjumska Optimizacija Ikompromisno Resenje (VIKOR) and Fuzzy Comprehensive Evaluation Method.

 (1) The technical suitability of prefabricated buildings is influenced by 7 core factors, such as adaptability of resources and environment, project planning and design level, and economic benefit; (2) When selecting the appropriate technology for prefabricated buildings, economic suitability should be considered first, followed by regional suitability, and then technical characteristic; (3) The prefabricated building technology suitability evaluation model constructed in this paper has high feasibility in the technical suitability selection of the example project.

The comprehensive evaluation model of prefabricated building technology suitability constructed in this paper provides technical selection support for the promotion and development of prefabricated buildings in different regions. In addition, the model can also be widely used in areas related to prefabricated building consulting and decision-making, and provides theoretical support for subsequent research.

This study provides a new decision support tool for prefabricated building technology suitability selection, which helps decision makers to make more rational technology choices.

This study has a positive impact on the advancement of prefabricated building technology, the improvement of construction industry standards, and the promotion of sustainable development.

The contribution of this study is twofold: (1) Theoretically, this paper provides technical evaluation indicators and guidelines for provincial and regional governments to cultivate model cities, plan industrial bases, etc. (2) In practice, it offers project-level appropriate technology system solutions for the technology application of assemblers in various regions.

This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to revenue generation.

The data envelopment analysis (DEA) approach was used to measure the relative operational efficiency of the studied assets in relation to the revenue performance. This method could produce a more informed and balanced approach to performance measurement.

The outcomes show that scores of efficiencies ranging from 7% to 99% in some of the models. The results showed that on average buildings are 75% relatively less efficient in maintenance, in term of revenue generation, than the benchmark set. Likewise, on average, the inefficient buildings are 60% relatively less efficient in insurance. Result also shows that 95% of the building assets in the sample are by and large operating at decreasing returns to scale. This implies that managers need to considerably reduce the operational resources (input) to improve the levels of revenue.

This study recommends that the FM operational variables that were found to inefficiently contribute to the revenue should be re-examined to test the validity of the findings. This is necessary before generalising or interpolating the results that are presented in this study.

The information obtained about operational performance can help FM managers to understand which improvements in the productivity of inefficient FM resources are required, providing insight into how to reduce operating costs and increase revenue.

This paper adds value in using new FM operational parameters to evaluate the efficiency of the performance of built assets.

Heritage building management serves as a potent catalyst for sustainability, yet it poses a distinctive set of challenges. Achieving a harmonious balance between conserving the building's historical and cultural value and ensuring modern functionality and safety remains a primary concern. The present work proposes a socio-technical approach to the development and use of a digital twin (DT) that will integrate social data related to the use of heritage buildings with building and environmental data.

The paper presents a logical and systematic joined-up management framework to the targeted heritage buildings, according to a “Whole Building” approach. Our approach is informed by the underpinning assumption that a heritage building and even more a heritage neighborhood is a socio-technical, complex and dynamic system, the change of which depends on the dynamic interconnections of materials, competences, resources, values, space/environment, senses and time.

A heritage dynamics approach is adopted to unfold the dynamic nature of heritage and to better inform decisions that can be made in the present and future, achieving people-centered and place-based heritage management. This proposition underlines the heritage transformation as a complex systemic process that consists of nonlinear interconnections of multiple heterogeneous factors (values, senses, attitudes, spaces and resources).

This paper presents a multi-level framework of DTs that interact hierarchically to comprehensively understand, assimilate and seamlessly integrate intricate contexts, even when faced with conflicting conditions from diverse cultural heritage entities. This paper outlines the importance of the iterative system dynamics (SD) approach, which enables adaptive management and ensures the resilience of cultural heritage over time.

This study aims to present the post-occupancy evaluation (POE) of a smart building on a university campus.

Literature review was conducted to report on the smart building concept and POE. A total of 49 performance indicators, classified according to the technical, functional and behavioral elements, were investigated. A case study of a smart building in a university campus was selected to assess the users’ satisfaction through POE. A total of 90 users from the academic and administrative staff of the building were contacted through a Web-based questionnaire survey, while 35 usable responses were obtained.

The findings revealed that the occupants were satisfied with several aspects of the building performance indicators; however, issues related to ventilation, control of thermostats, amount of natural lighting through low-E windows and privacy were identified. Ultimately, the study reveals that, although the adoption of smart technologies has the potential to provide an excellent and comfortable work environment, the efficient selection and/or maintenance of the adopted technologies is crucial.

The concept of smart buildings is perceived to be the future of the industry. Despite this growing need for “smartization,” the performance of a building ultimately depends on how it satisfies the needs of those who use them. The value and implication of this study is inherent in its emphasis on the satisfaction of buildings users in futuristic designs of buildings. The study shows that POEs can be used to assess the satisfaction of users of smart buildings to understand what works, as well as what needs redesign or improvement.

A tangible representation of the housing market hotness, namely, the index derived via Carrilo 2013’s concept, is widely used but difficult to construct, as it requires listing data of a geography which is not only inaccessible but also cumbersome to summarize. The purpose of this paper is to serve as a methodological extension of Carrilo 2013, presenting a process by which the seller’s bargaining power index can be easily constructed using open-source data.

The seller’s bargaining power index was first constructed using the open-source data by Zillow Research. Then, results verification approach including visualization, Pearson correlation test, Granger causality test and linear regression were used with the goal to generate empirical evidence and verify if the constructed index conforms with established relationships derived in past work, thereby substantiate accuracy of the constructed index.

Monthly seller’s bargaining power index was constructed for US as a whole and 250 metropolitan statistical areas through an automated process. The constructed index conforms with established evidence in terms of seasonality, trend and magnitude. The index also forms positive correlation with building permit/housing value/housing value increase rate, and negative correlation with inventory. It requires the explanatory power of multiple linear features to simulate the parameter. Seller’s bargaining power index at a national level can serve as a general indicator of economic strength, in parallel to number of building permit, housing starts and real gross domestic product. Quantitatively, the housing market of the USA is as “hot” during the COVID-19 pandemic as it was in the years before 2008. A healthy housing market appears to be one in which 75%−80% of the deals are in favor of the seller (take-it-or-leave-it). When said percentage reaches 90%, the market should be considered to be in a “bubble.”

This paper provides an easy pathway for future studies to simulate housing market hotness, and bring upon the benefit of convenience at low cost and in monthly frequency rather than quarterly or annually.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

The purpose of this study is to develop a set of parameters universally acceptable for assessing design and construction strategies for reducing operational energy usage and its associated greenhouse gas (GHG) emission. Also, the parameters are intended to estimate the quantity of energy and its associated GHG emission reduction over the assessment period.

This study used five steps framework comprising definition of purpose, selecting the candidate parameters, criteria selection and description, selecting proposed parameters and defining the proposed parameters. The criteria used were the parameter’s prevalence, measurability, preference and feasibility toward adaptability to the relevant stakeholders.

This study consolidated 11 parameters. Seven cover designs and construction strategies comprising energy monitoring, natural lighting and ventilation design. Others are building thermal performance, efficient equipments, renewable energy and energy policy. The remaining four consider operational energy consumption, GHG emission quantification and their reduction over time.

Providing suitable indicators for assessing direct and indirect GHG emission with easily accessible data is essential for assessing built environment. The consolidated parameters can be used in developing rating systems, monitoring GHG inventories and activities of building related industries.

This study was conducted at the CEIES UTHM and used 11 existing rating systems open for research purposes, International Panel for Climate Change reports and GHG protocol report and guides and several other standards.

The paper aims at identifying the underlying factors that differentiate the local people's demographic and socio-economic characteristics, which influence their sense of place (SoP) toward different types of cultural heritage sites in different urban regions.

The paper draws on the sequential explanatory design, using quantitative and qualitative methods, respectively, to investigate the research themes in-depth. A total of 201, 207 and 228 questionnaires were collected on religious, non-religious/memorial and historic commercial buildings, respectively, at two different quarters in Historic Cairo, followed by 20 semi-structured interviews with a representative sample of local people in each quarter.

The authors found an apparent similarity in the significant characteristics that influence the local people's SoP levels toward the different historic buildings at the same quarter and a notable variation between both quarters. Also, many factors determine the local people's influential characteristics on their SoP toward the different cultural heritage sites (e.g. the sites’ distance from the person's residence/workplace, economic value, people's awareness about its history, type of activities and targeted gender, feeling of stability and regions' characteristics)

Identifying these underlying factors and priority local groups can assist policymakers in ensuring a sustainable management/conservation of the different cultural heritage sites.

The paper demonstrates the causality of a significant correlation between local people's characteristics and their SoP levels toward the different types of historic buildings, apart from their religious symbolism or historical value, using the sequential explanatory design.

Building permitting is mostly a manual, labor intensive and time-consuming process. Initiatives for streamlining the process are not always helpful since they often fail to address the core problems within the process. A framework for modeling the permitting process can be useful to identify bottlenecks, core challenges and best practices. Hence, the authors aim to demonstrate and validate a previously suggested workflow for permit process modeling using the permitting process in Israel as a test case.

The authors implement qualitative expert interviews for data acquisition. The collected data are then processed for a qualitative data analysis. The results of the analysis are then validated using a focus group workshop in the field of building permits. In the test case the focus group consisted of Israeli experts.

The authors present a detailed overview of the as-is building permit process in Israel and the existing challenges. Through this test case, the authors found that the framework is applicable in different countries and that it can provide valuable insights into the core problems within the process. In addition, application of the same framework in different countries can provide comparable results that would allow the authors to identify best practices.

The major contribution of this work is the development and validation of a framework for building permitting process modeling which can be used to identify existing challenges and bottlenecks in the process. Implementing a structured and unified approach provides an opportunity to easily compare processes in different countries to identify best practices.