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This book brings together the expertise of a unique team of researchers and methods to examine the role of real estate, space and land use in crisis management and preparedness.

Zusammenfassung: This book provides a comprehensive global coverage of energy management as it relates mostly to developing countries. In an era of unprecedented global population growth, the demand for energy has reached staggering levels.

The construction industry in developing countries faces significant challenges, including limited resources, infrastructure constraints, and varying levels of technological readiness, which hinder the adoption of Building Information Modelling. Despite the recognized benefits in enhancing project efficiency, reducing costs, and improving collaboration, there is a lack of comprehensive understanding and tailored strategies for Building Information Modelling implementation in these contexts. This study aims to address these gaps by systematically reviewing Building Information Modelling adoption literature from 2013 to 2023 and developing a model specifically designed to guide its integration in developing nations. It categorizes nations by demographic and economic parameters and employs thematic analysis to explore benefits, challenges, and strategies. The study develops a tailored Building Information Modelling adoption model, by highlighting the dynamic interplay between adoption, contextual and strategic factors, technological advancement and socio-economic development and synthesizing key insights from academic literature and empirical findings. It offers strategic recommendations for nations at different Building Information Modelling integration stages, addressing their unique socio-economic and environmental contexts. The study identifies trends in utilization and highlights significant gaps, particularly in empirical, educational and strategic initiatives on digital construction transformation.

The primary drawback of concrete lies in its low tensile strength, prompting the development of various solutions to enhance this aspect. A notable approach is the utilization of Prestressed Reinforced Concrete (PRC) with tendons, aimed at bolstering its tensile strength. As the use of diverse tendon types in the PRC continues to surge, a review becomes imperative to delve into this evolution. Therefore, this study delved into the engineering characteristics, performance, and evolution of different tendon varieties, encompassing both steel and composite options. Despite certain drawbacks associated with employing composite materials such as Fiber Reinforced Polymer (FRP) tendons - such as heightened costs, limited availability of composite materials, and intricate manufacturing processes - there are distinct advantages and merits to incorporating FRP composite tendons in the realm of construction. In this respect, Carbon FRP tendons exhibited superior strength, comparable to their steel counterparts. Glass FRP tendons, lacking metallic components, possessed non-magnetic properties, rendering them resistant to corrosion. Additionally, Aramid FRP tendons boasted low flammability and exceptional resistance to elevated temperatures. Lastly, Basalt FRP tendons offered sustainability, rust resistance, and non-corrosiveness. The findings derived from this review study serve as a valuable resource for researchers seeking to advance the applications of steel tendons and FRP composite materials within the construction industry.

PurposeIn the study, a five-dimensional-safety risk assessment model (5D-SRAM) is developed to improve the construction safety risk assessment approaches available in the literature. To that purpose, a hybrid multi-dimensional fuzzy-based model is proposed, which provides a comprehensive ranking system for the safety risks existing in a project by considering the contextualization of the construction-related activities resulting in an accident.Design/methodology/approachThe developed 5D-SRAM is based on an amalgamation of different fuzzy-based techniques. Through the proposed fuzzy analytic hierarchy process (AHP) method, the importance weights of essential risk dimensions playing role in defining the magnitude of the construction-related risks are obtained, while a precise prioritized ranking system for the identified safety risks is acquired using the proposed fuzzy technique of order preference similarity to the ideal solution (FTOPSIS).FindingsThrough the application of the proposed 5D-SRAM to a real-life case study – which is the case of green building construction projects located in Hong Kong – contributions are realized as follows: (1) determination of a more complete range of risk dimensions, (2) calculation of importance weightings for each risk dimension and (3) obtainment of a precise and inclusive ranking system for safety risks. Additionally, the supremacy of the developed 5D-SRAM against the other safety assessment approaches that are commonly adopted in the construction industry is proved.Research limitations/implicationsThe developed 5D-SRAM provides the concerned safety decision-make

A courtyard is an architectural design element which is often known as microclimate modifiers and is responsible to increase the indoor occupant comfort in traditional architecture. The aim of this study is to conduct a parametric evaluation of courtyard design variants in a residential building of different climates with a focus on indoor thermal comfort and utility costs. A brute-force approach is applied to generate a wide range of design alternatives and the simulation workflow is conducted by Grasshopper together with the environmental plugins Ladybug and Honeybee. The main study objective is the evaluation of the occupant thermal comfort in an air-conditioned residential building, energy load, and cost analysis, derived from different design variables including courtyard geometry, window-to-wall ratio, envelope materials, heating, and cooling set-point dead-bands, and building geographical location. Furthermore, a Deep Learning model is developed using the inputs and outputs of the simulation and analysis to transform the outcomes into the algorithmic and tangible environment feasible for predictive applications. The results suggest that regarding the thermal loads, costs, and indoor thermal comfort index (PMV), there are high correlations between the outdoor weather variation and dead-band ranges, while in extreme climates such as Singapore, courtyard spaces might not be efficient enough as expected. Finally, the highly accurate deep learning model is also developed, delivering superior predictive capabilities for the thermal comfort and utility costs of the courtyard designs.

© 2017 American Society of Civil Engineers. This study develops an integrated model of the main contextual factors that affect sustainable delivery of megaprojects in Iran. The inputs to the model are based on a comprehensive literature review affecting the "triple bottom line" of sustainability, as measured in economic, environmental, and social costs. Innovation diffusion theory and extralogical laws of imitation inform the theoretical points of departure. The model inputs were customized for the context of Iran, and a structural equation model was developed, using data collected from 101 survey questionnaires. The findings identify a wide range of factors that directly impact megaproject sustainability, and these may be used by policymakers and practitioners within the Iranian construction industry to better manage sustainability outcomes. Principally, megaprojects would benefit from (1) the generation of sustainability awareness at the evaluation phase, (2) tackling corruption at the preparation phase, and (3) consolidating responsible project management practices at the usage phase. Moreover, these lessons may be more broadly applicable across the entire construction supply chain in Iran, as well as being transferable to other developing countries.

© 2018, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature. This study reports on developing an innovative approach for the parametric analysis of daylighting and visual comfort, through a sun responsive shading system. The objective is estimating the annual daylight metrics and indoor glare discomfort. To this end, a review of the literature was carried out on three key concepts: smart facades, visual comfort, and parametric design, in order to develop a dynamic pattern of an oriental system for enhancing the daylight and visual performance. Afterwards, two geometrical components (Rosette modules and louvers) were applied, using Grasshopper plug-in for Rhino and daylighting plug-in DIVA, to investigate the indoor daylight quality through different geometrical and physical properties. This resulted in generating 6480 design variants, when several variables (rotation, distance to facade, time hours, transmittance properties and colors) that affect incoming daylight as well as visual comfort performance in a single office space in the hot-arid climate of Tehran were taken into account. Interactive correlations between the overall performance of kinetic patterns and visual performance were investigated through an optimization process. Analyses showed that the proposed approach is capable of significantly improving the shading flexibility to control daylight metrics and glare, via a full potential adaptive pattern to achieve the maximum visual comfort level based on LEEDv4 certificate.

© 2017 Elsevier B.V. The construction industry is under pressure as to reduce the sizable quantities of construction waste generated during construction operations. Modular coordination (MC) and parametric design both have great potentials in reducing waste at the design stage. And anecdotal evidence suggests that great volumes of waste can be reduced through integration of parametric design into MC. The issue of proposing workflows in this context, however, is under-researched, and practical applications, if any, are at the rhetorical stage. To accomplish this, an integration attempt is made in this study to provide the details of a developed-and-experimented workflow for this purpose; a generative algorithm is developed through the Rhinoceros 3D–Grasshopper platform, subject to MC rules. Two sets of horizontal and vertical modules are obtained from a prototype model, while an evolutionary solver function is applied in reducing the generated construction waste volume. According to a pre-defined standard specification, different modular design variants that fully conform to the design constraints in modules array are developed, providing an operational workflow in construction waste reduction at the design stage. Introducing this workflow, and how the proposed workflow reduces the volume of post-optimization paneling waste by 2% at its minimum are the major findings here. The insights derived from this study, would promote the interest of both the construction practitioners and researchers; the practicality of integrating parametric design into MC is proven.

© 2018 American Society of Civil Engineers. Building information modeling (BIM) has developed as the definitive technology for managing construction projects. With its rise, the corresponding role of BIM manager has emerged as a necessary adjunct role in coordinating BIM-enabled projects. The ascent of the BIM manager has attracted a significant body of research, investigating the various competencies and responsibilities required of the role. While BIM is here to stay, a recent study, however, asserts that a distinct role oriented around BIM is transitory, which represents a significant departure from accepted assumptions regarding the viability of the BIM manager role. This research sets out to test the likelihood of a long-term market demand for the BIM manager, as a distinct role, based on a robust quantitative analysis of open-source data from a rich empirical dataset of global relevance for North America, Europe, and Australasia. Text mining methods are used. A total of 199 BIM-related jobs were retrieved from 14 of the most relevant job websites, representing the global English speaking job markets. Key knowledge, skills, and abilities that are attributes of BIM jobs were extracted and analyzed. Analysis reveals that there is no significant difference between the roles BIM manager and BIM coordinator. Moreover, the findings highlight that these two BIM roles align with that of project manager. Most importantly, analysis shows that BIM roles supplement the lack of BIM expertise within the role of project manager, and that, as BIM capabilities are increasingly absorbed by project managers, the rationale for an independent BIM expert will fade. The corollary is that BIM roles are a stopgap measure that can be expected to disappear as project managers absorb requisite BIM skills. The practical implication for construction engineering HR departments is that the prevailing policy of retaining dedicated BIM managers into the longer-term future should shift to one in

© 2017 The Authors. Artificial intelligence algorithms have been applied separately or integrally for prediction, classification or optimization of buildings energy consumption. However, there is a salient gap in the literature on the investigation of hybrid objective function development for energy optimization problems including qualitative and quantitative datasets in their constructs. To tackle with this challenge, this paper presents a hybrid objective function of machine learning algorithms in optimizing energy consumption of residential buildings through considering both continuous and discrete parameters of energy simultaneously. To do this, a comprehensive dataset including significant parameters of building envelop, building design layout and HVAC was established, Artificial Neural Network as a prediction and Decision Tree as a classification algorithm were employed via cross-training ensemble equation to create the hybrid function and the model was finally validated via the weighted average of the error decomposed for the performance. The developed model could effectively enhance the accuracy of the objective functions used in the building energy prediction and optimization problems. Furthermore, the results of this novel approach resolved the inclusion issue of both continuous and discrete parameters of energy in a unified objective function without threatening the integrity and consistency of the building energy datasets.