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The residential buildings sector has a high priority in the climate change adaptation process due to significant CO₂ emissions, high energy consumption and negative environmental impacts. The article investigates how, conversely speaking, the residential buildings will be affected by climate change, and how to improve existing structures and support long-term decisions.

The climate dataset was created using the scenarios determined by the Intergovernmental Panel on Climate Change (IPCC), and this was used in the study. Different building envelope and Heating, Ventilating and Air Conditioning (HVAC) systems scenarios have been developed and simulated. Then, the best scenario was determined with comparative results, and recommendations were developed.

The findings reveal that future temperature-increase will significantly impact buildings' cooling and heating energy use. As the outdoor air temperatures increase due to climate change, the heating loads of the buildings decrease, and the cooling loads increase significantly. While the heating energy consumption of the house was calculated at 170.85 kWh/m² in 2020, this value shall decrease significantly to 115.01 kWh/m² in 2080. On the other hand, the cooling energy doubled between 2020 and 2080 and reached 106.95 kWh/m² from 53.14 kWh/m² measured in 2020.

Single-family houses constitute a significant proportion of the building stock. An in-depth analysis of such a building type is necessary to cope with the devastating consequences of climate change. The study developed and scrutinised energy performance improvement scenarios to define the climate change adaptation process' impact and proper procedure. The study is trying to create a strategy to increase the climate resistance capabilities of buildings and fill the gaps in this regard.

This paper proposes an intact framework for building life cycle energy estimation (LCEE), which includes three major energy sources: embodied, operational and mobile.

A systematic review is conducted to summarize the selected 109 studies published during 2012–2021 related to quantifying building energy consumption and its major estimation methodologies, tools and key influence parameters of three energy sources.

Results show that the method limitations and the variety of potential parameters lead to significant energy estimation errors. An in-depth qualitative discussion is conducted to identify research knowledge gaps and future directions.

With societies and economies developing rapidly across the world, a large amount of energy is consumed at an alarming rate. Unfortunately, its huge environmental impacts have forced many countries to take energy issues as urgent social problems to be solved. Even though the construction industry, as the one of most important carbon contributors, has been constantly and academically active, researchers still have not arrived at a clear consensus for system boundaries of life cycle energy. Besides, there is a significant difference between the actual and estimated values in countless current and advanced energy estimation approaches in the literature.

This paper aims to investigate the theoretical and practical links between digital twin (DT) application in heritage facilities management (HFM) from a life cycle management perspective and to signpost the future development directions of DT in HFM.

This state-of-the-art review was conducted using a systematic literature review method. Inclusive and exclusive criteria were identified and used to retrieve relevant literature from renowned literature databases. Shortlisted publications were analysed using the VOSviewer software and then critically reviewed to reveal the status quo of research in the subject area.

The review results show that DT has been mainly adopted to support decision-making on conservation approach and method selection, performance monitoring and prediction, maintenance strategies design and development, and energy evaluation and management. Although many researchers attempted to develop DT models for part of a heritage building at component or system level and test the models using real-life cases, their works were constrained by availability of empirical data. Furthermore, data capture approaches, data acquisition methods and modelling with multi-source data are found to be the existing challenges of DT application in HFM.

In a broader sense, this study contributes to the field of engineering, construction and architectural management by providing an overview of how DT has been applied to support management activities throughout the building life cycle. For the HFM practice, a DT-cum-heritage building information modelling (HBIM) framework was developed to illustrate how DT can be integrated with HBIM to facilitate future DT application in HFM. The overall implication of this study is that it reveals the potential of heritage DT in facilitating HFM in the urban development context.

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.

Project-based learning is one of the most effective methods of transferring academic knowledge and skills to real-world situations in higher education. However, its effectiveness is not much investigated focusing on the students' narrative. This study aims at evaluating the students' experience and perspective on adopting project-based learning in master by research and doctoral programmes for proactive skills development.

This study evaluates the self-reflection of 10 postgraduate students and their supervisor who have participated in developing a software tool for solar photovoltaics (PV) integrated building envelope design, management and the related education.

Findings reveal that the students have effectively improved their knowledge on the subject via collaborating with the industry, self-learning/observation, peer learning, problem-solving and teamwork. Dividing the project into student-led tasks has improved the decision-making and leadership skills, risks identification, planning and time management skills. The overall experience has (1) built up confidence in students, (2) enhanced their creativity and critical thinking and (3) improved their proactive skills and context knowledge.

A clear research gap can be seen in exploring the effectiveness of project-based learning for master by research and doctoral programmes, which mainly focus on extensive research. These programmes do not necessarily focus on developing students' proactive skills, which is the main requirement if they intend to work in the construction industry. This paper addresses the above research gap by demonstrating the effectiveness of project-based learning for developing the proactive skills in a research-intensive learning environment.

Many parameters influence the success of sustainable projects (SPs) in the architecture–engineering–construction. One of these important parameters is the project delivery attributes (PDA), which are influenced by the project delivery system (PDS) while affecting the selection of it. This study aims to evaluate the significance of PDAs in influencing both the performance and success of SPs in Turkey, where the interest in SPs is high.

The impact of PDAs was determined by applying the two-round Delphi method with a semistructured interview involving the main stakeholders of a construction project, like owners, designers, contractors and consultants who played active roles in SPs, as well as academics to theoretically evaluate the issue. The significance of PDAs was assessed using the relative importance index, and the results were validated using the interrater agreement analysis.

The study identified key PDAs impacting SPs as owner character, commitment and motivation; simulation and energy modelling; and timing of stakeholders/early involvement.

The investigation of the significance of the PDAs is a lesser-studied context. Therefore, a research framework that enables an effective set of methods for solving the sectoral problems of PDAs that have impacts on SPs has been proposed. The framework is expected to open new opportunities for the generation and regulation of the PDSs for SPs. The findings will provide valuable insights to project stakeholders, particularly owners, local authorities and policymakers to assess which PDAs have a greater impact on sustainability performance when setting PDSs in SPs in other developing countries.

Governments in developing countries manage their considerable state assets for public service delivery directly. In Indonesia, the Directorate of State Asset Management responsible for developing the national strategy for state asset optimization requires the determination of key elements and prioritization tools. The purpose of this paper is to show that a simple calculation using the combination of the balanced scorecard (BCS) and analytical hierarchy process (AHP) will help in the prioritization of strategy development.

A questionnaire survey of 131 multistakeholder respondents to identify the most important key elements and the best alternative for asset optimization was done in this study.

The respondents agree on the most important key elements, and that the best alternative for asset optimization is the efficient maintenance of assets. Competitive human resources comprise the recommended second key element, and that improvements in asset performance and value will improve public service as the second-highest alternative. This study also shows the importance of the integration of asset optimization in existing government strategic instruments supported by a comprehensive data set related to public assets and their performance.

This paper provides a new contribution to integrating asset optimization strategies as the core of the organization’s performance and prioritization strategies. Additional BSC perspectives are suggested, with the inclusion of AHP for prioritization. In addition, this study includes the opinions of all the stakeholders, from external users to the central management. The flexibility of the tools to adapt to the existing strategic framework will allow their application by different agencies and in different countries.

Energy consumption in existing office buildings has been growing in parallel with the rise in occupant energy demand. As a result, many building owners have given smart retrofits (SRs) a higher priority. However, the utilisation of suitable SRs from a range of SRs has become a challenging task. The purpose of this paper is to develop a decision-making model to select the most suitable SRs for conventional office buildings and form a set of benchmarks for assessing the performance of SRs.

A qualitative approach with six case studies was used. Content analysis was carried out using NVivo to explore the factors considered for the selection of SR techniques. A decision-making model for selecting SRs in Sri Lankan office buildings was proposed. SR performance benchmarks were developed by referring to established standards and studies done in tropical office buildings.

Out of 18 identified SRs from literature, fan cycling, ventilation control and LED luminaires have been recognised as commonly used SRs in Sri Lankan office buildings. Analysis showed that HVAC retrofits saved more energy, while lighting retrofits could be easily implemented in existing buildings. The proposed decision-making model can explore further improvements to enhance the performance of SRs.

The selection of SRs is a comprehensive decision-making process. Metrics were established to benchmark the performance of SRs. The proposed model offers a tool for building owners and facility managers to optimise facility operations.

Digital twin (DT) and building information modeling (BIM) are interconnected in some ways. However, there has been some misconception about how DT differs from BIM. As a result, industry professionals reject DT even in BIM-based construction projects due to reluctance to innovate. Furthermore, researchers have repeatedly developed tools and techniques with the same goals using DT and BIM to assist practitioners in construction projects. Therefore, this study aims to assist industry professionals and researchers in understanding the relationship between DT and BIM and synthesize existing works on DT and BIM.

A systematic review was conducted on published articles related to DT and BIM. A total record of 54 journal articles were identified and analyzed.

The analysis of the selected journal articles revealed four types of relationships between DT and BIM: BIM is a subset of DT, DT is a subset of BIM, BIM is DT, and no relationship between BIM and DT. The existing research on DT and BIM in construction projects targets improvements in five areas: planning, design, construction, operations and maintenance, and decommissioning. In addition, several areas have emerged, such as developing geo-referencing approaches for infrastructure projects, applying the proposed methodology to other construction geometries and creating 3D visualization using color schemes.

This study contributed to the existing body of knowledge by overviewing existing research related to DT and BIM in construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.