The performance gap in energy-efficient office buildings: how the occupants can help?

Rising demand and limited production of electricity are instrumental in spreading the awareness of cautious energy use, leading to the global demand for energy-efficient buildings. This compels the construction industry to smartly design and effectively construct these buildings to ensure energy performance as per design expectations. However, the research tells a different tale: energy-efficient buildings have performance issues. Among several reasons behind the energy performance gap, occupant behavior is critical. The occupant behavior is dynamic and changes over time under formal and informal influences, but the traditional energy simulation programs assume it as static throughout the occupancy. Effective behavioral interventions can lead to optimized energy use. To find out the energy-saving potential based on simulated modified behavior, this study gathers primary building and occupant data from three energy-efficient office buildings in major cities of Pakistan and categorizes the occupants into high, medium, and low energy consumers. Additionally, agent-based modeling simulates the change in occupant behavior under the direct and indirect interventions over a three-year period. Finally, energy savings are quantified to highlight a 25.4% potential over the simulation period. This is a unique attempt at quantifying the potential impact on energy usage due to behavior modification which will help facility managers to plan and execute necessary interventions and software experts to develop effective tools to model the dynamic usage behavior. This will also help policymakers in devising subtle but effective behavior training strategies to reduce energy usage. Such behavioral retrofitting comes at a much lower cost than the physical or technological retrofit options to achieve the same purpose and this study establishes the foundation for it

Barriers to the digitalisation and innovation of Australian Smart Real Estate: A managerial perspective on the technology non-adoption

The real estate sector brings a fortune to the global economy. But, presently, this sector is regressive and uses traditional methods and approaches. Therefore, it needs a technological transformation and innovation in line with the Industry 4.0 requirements to transform into smart real estate. However, it faces the barriers of disruptive digital technology (DDT) adoption and innovation that need effective management to enable such transformation. These barriers present managerial challenges that affect DDT adoption and innovation in smart real estate. The current study assesses these DDTs adoption and innovation barriers facing the Australian real estate sector from a managerial perspective. Based on a comprehensive review of 72 systematically retrieved and shortlisted articles, we identify 21 key barriers to digitalisation and innovation. The barriers are grouped into the technology-organisation-external environment (TOE) categories using a Fault tree. Data is collected from 102 real estate and property managers to rate and rank the identified barriers. The results show that most of the respondents are aware of the DDTs and reported AI (22.5% of respondents), big data (12.75%) and VR (12.75%) as the most critical technologies not adopted so far due to costs, organisation policies, awareness, reluctance, user demand, tech integration, government support and funding. Overall, the highest barrier (risk) scores are observed for high costs of software and hardware (T1), high complexity of the selected technology dissemination system (T2) and lack of government incentives, R&D support, policies, regulations and standards (E1). Among the TOE categories, as evident from the fault tree analysis, the highest percentage of failure to adopt the DDT is attributed to E1 in the environmental group. For the technological group, the highest failure reason is attributed to T2. And for the organisational group, the barrier with the highest failure chances for DDT adoption is the lack of organisational willingness to invest in digital marketing (O4). These barriers must be addressed to pave the way for DDT adoption and innovation in the Australian real estate sector and move towards smart real estate

It's all about perceptions: A DEMATEL approach to exploring user perceptions of real estate online platforms

Real Estate Online Platforms (REOPs) are used for conveying real estate and property-related information to potential users (buyers, renters, or sellers). The information leveraged through REOPs supports these users in reaching conclusive rent or buy decisions. Despite their promised utility, user perception about accepting online information through REOPs is unexplored. Using a comprehensive questionnaire and data collected from 65 users, the current study captures the users’ perception of REOPs. Risk, service, information, system, technology adoption model (RSISTAM) is proposed comprising of seven users’ perceptions: risk (PR), service quality (PSEQ), information quality (PIQ), and system quality (PSYQ) from the information systems success model, and usefulness (PU), ease of use (PEU) and behaviour to accept (BAU) from TAM. The results are analysed using the decision making trial and evaluation laboratory (DEMATEL) approach, which shows that PIQ, PSEQ and PEU are the causes and PR, PSYQ, PU and BAU are the effects. Among the criteria, the order of prominence is PEU > PSEQ > PIQ, and for net effects, the order is PU > BAU > PSYQ > PR. For addressing the causes, the REOP managers must provide more transparent, high quality and voluminous information to the users, focus on the system, services, and information qualities, and add more enjoyable, immersive and easy-to-use content through REOPs. This study contributes to the body of knowledge by exploring user perceptions and proposing methods to improve the quality and reliability of REOPs in line with Real Estate 4.0 and industry 4.0 aims

Lean Practices Using Building Information Modeling (BIM) and Digital Twinning for Sustainable Construction

There is a need to apply lean approaches in construction projects. Both BIM and IoT are increasingly being used in the construction industry. However, using BIM in conjunction with IoT for sustainability purposes has not received enough attention in construction. In particular, the capability created from the combination of both technologies has not been exploited. There is a growing consensus that the future of construction operation tends to be smart and intelligent, which would be possible by a combination of both information systems and sensors. This investigation aims to find out the recent efforts of utilizing BIM for lean purposes in the last decade by critically reviewing the published literature and identifying dominant clusters of research topics. More specifically, the investigation is further developed by identifying the gaps in the literature to utilize IoT in conjunction with BIM in construction projects to facilitate applying lean techniques in a more efficient way in construction projects. A systematic review method was designed to identify scholarly papers covering both concepts “lean” and “BIM” in construction and possibilities of using IoT. A total of 48 scholarly articles selected from 26 construction journals were carefully reviewed thorough perusal. The key findings were discussed with industry practitioners. The transcriptions were analyzed employing two coding and cluster analysis techniques. The results of the cluster analysis show two main directions, including the recent practice of lean and BIM interactions and issues of lean and BIM adoption. Findings revealed a large synergy between lean and BIM in control interactions and reduction in variations, and surprisingly there are many uncovered areas in this field. The results also show that the capability of IoT is also largely not considered in recent developments. The number of papers covering both lean and BIM is very limited, and there is a large clear gap in understanding synergetic interactions of lean concepts applying in BIM and IoT in specific fields of construction such as sustainable infrastructure projects

Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors

Construction Technology Adoption Cube: An Investigation on Process, Factors, Barriers, Drivers and Decision Makers Using NVivo and AHP Analysis

Due to the complexity, high-risk and conservative character of construction companies, advanced digital technologies do not become widely adopted in the short term, while vendors make determined efforts to overcome this and disseminate their technologies. This paper presents the methods of an investigation addressing the extremely complex issues related to the current practices of digital technology adoption in construction. It discusses how construction companies follow a specific logical process linked to need, project objectives, the characteristics of the adopting organization and the characteristics of the new technology to be adopted. The study aims to demonstrate a novel method of data collection and analysis, such as data and methodological triangulation techniques, including the use of NVivo and AHP to explore how companies make the decision to uptake new technology (e.g., advanced crane, tunnel boring machine or drones) by focusing on customer and vendor activities, their interactions, contributing factors and people involved in the process. The major original contribution of this paper is developing an innovative methodological cube for investigating the Construction Technology Adoption Process (CTAP) covering technology adoption, acceptance, diffusion and implementation concepts. CTAP is a framework that delineates the phases of the process that customer organizations use when deciding to adopt a new digital technology and the parallel vendor activities. The significance of these contributions is that they enable vendors to understand how to match their strategies with customer expectations in each phase of CTAP. It also provides a benchmark for new construction companies to use the current best practices of decision making. Future research is warranted to more clearly delineate any differences with respect to developing nations or related industries such as mining and property management

Delay Causes and Emerging Digital Tools: A Novel Model of Delay Analysis, Including Integrated Project Delivery and PMBOK

Delay is one of the main challenges of construction projects, and there is still much to overcome in order to reach near zero delay in all construction projects. This project aims to conduct a systematic critical review including a bibliography analysis on delay literature in construction. The main questions consider what has been learnt from a decade investigating delay causes and effects in the construction literature and what factors have been missed in the literature. This paper also presents a new and challenging question regarding how digital tools and associated technologies may prevent any delay in construction projects, which can change the research direction from delay investigations to identifying prevention factors. The paper identifies the delay dataset, including 493 papers investigating delay in construction, and establishes a specific dataset of papers focusing on delay effects and causes (DEC), including 94 selected papers covering different factors examined in over 29 countries such as Iran, India, Turkey, Bangladesh, Saudi Arabia, the United Arab Emirates (UAE), Cambodia, Oman, Malaysia, Taiwan, China, Vietnam, the US, the UK, and Egypt. In addition, the paper identifies 30 critical factors with the frequency of occurrences over three times in the DEC dataset and computes their medians of ranking. This paper also discusses digital tools and methods that can be used for delay analysis and preventions, including MS Project, Oracle Primavera P6, and Open Plan by Deltek. The paper discusses the project schedule delay analysis from project management methodology perspectives. It also discusses the current method’s limitations and future directions, which are based on the identification of the deficiency areas. In total, four overlooked factors are identified and suggested, including faulty data analysis, unmatched structure of the research questionnaires with new knowledge and standards [e.g., Project Management Body of Knowledge (PMBOK)], overlooked effects of digital technologies [e.g., Digital twin, Navisworks, Building Information Model (BIM), Geographic Information System (GIS), and Integrated Project Delivery (IPD)], and ignored job-site technologies. In addition, the paper presents the DEC model for future studies, including four main key factors. These factors are resources (e.g., project budgets, labour, material, equipment, and digital tool), project context, stakeholders performance (e.g., owner/client, consultant/designer, contractor, vendor/supplier), and external factors (e.g., ground condition, site location, regulation, natural disaster), which may significantly affect delay prevention and should be concurrently considered in the future delay investigations, since they may be required for designing an effective mitigation strategy when these proof points are identified. This would significantly help to utilise digital systems to prevent time overruns in different construction contexts

Spatial Analysis Using Temporal Point Clouds in Advanced GIS: Methods for Ground Elevation Extraction in Slant Areas and Building Classifications

Deriving 3D urban development patterns is necessary for urban planners to control the future directions of 3D urban growth considering the availability of infrastructure or being prepared for fundamental infrastructure. Urban metrics have been used so far for quantification of landscape and land-use change. However, these studies focus on the horizontal development of urban form. Therefore, questions remain about 3D growth patterns. Both 3D data and appropriate 3D metrics are fundamentally required for vertical development pattern extraction. Airborne light detection and ranging (Lidar) as an advanced remote-sensing technology provides 3D data required for such studies. Processing of airborne lidar to extract buildings’ heights above a footprint is a major task and current automatic algorithms fail to extract such information on vast urban areas especially in hilly sites. This research focuses on proposing new methods of extraction of ground points in hilly urban areas using autocorrelation-based algorithms. The ground points then would be used for digital elevation model generation and elimination of ground elevation from classified buildings points elevation. Technical novelties in our experimentation lie in choosing a different window direction and also contour lines for the slant area, and applying moving windows and iterating non-ground extraction. The results are validated through calculation of skewness and kurtosis values. The results show that changing the shape of windows and their direction to be narrow long squares parallel to the ground contour lines, respectively, improves the results of classification in slant areas. Four parameters, namely window size, window shape, window direction and cell size are empirically chosen in order to improve initial digital elevation model (DEM) creation, enhancement of the initial DEM, classification of non-ground points and final creation of a normalised digital surface model (NDSM). The results of these enhanced algorithms are robust for generating reliable DEMs and separation of ground and non-ground points in slant urban scenes as evidenced by the results of skewness and kurtosis. Offering the possibility of monitoring urban growth over time with higher accuracy and more reliable information, this work could contribute in drawing the future directions of 3D urban growth for a smarter urban growth in the Smart Cities paradigm

Construction Technology Adoption Cube: An Investigation on Process, Factors, Barriers, Drivers and Decision Makers Using NVivo and AHP Analysis

Due to the complexity, high-risk and conservative character of construction companies, advanced digital technologies do not become widely adopted in the short term, while vendors make determined efforts to overcome this and disseminate their technologies. This paper presents the methods of an investigation addressing the extremely complex issues related to the current practices of digital technology adoption in construction. It discusses how construction companies follow a specific logical process linked to need, project objectives, the characteristics of the adopting organization and the characteristics of the new technology to be adopted. The study aims to demonstrate a novel method of data collection and analysis, such as data and methodological triangulation techniques, including the use of NVivo and AHP to explore how companies make the decision to uptake new technology (e.g., advanced crane, tunnel boring machine or drones) by focusing on customer and vendor activities, their interactions, contributing factors and people involved in the process. The major original contribution of this paper is developing an innovative methodological cube for investigating the Construction Technology Adoption Process (CTAP) covering technology adoption, acceptance, diffusion and implementation concepts. CTAP is a framework that delineates the phases of the process that customer organizations use when deciding to adopt a new digital technology and the parallel vendor activities. The significance of these contributions is that they enable vendors to understand how to match their strategies with customer expectations in each phase of CTAP. It also provides a benchmark for new construction companies to use the current best practices of decision making. Future research is warranted to more clearly delineate any differences with respect to developing nations or related industries such as mining and property management

Selecting Optimal Project Delivery System for Infrastructural Projects Using Analytic Hierarchy Process

Abstract Enormous financial requirements, high technological and scientific level, high work volume are the main differentiations between infrastructural andnormal projects. Given these specifications, selection of suitable project delivery system (PDS) for construction of infrastructural projects is of paramount importance, and making mistake and miscalculation in this field will incur irreparable loss to the development projects. Therefore, adoption of suitable policy in decision-making process with regard to the construction operation of project is of paramount significance, so that development of systems and logistics tools for the decision-making process can help project commissioners to improve current activities in the best form possible. The paper presents a decision-making model to select the optimal PDS for infrastructural construction projects based on identification and comparison of various contracting systems. The viewpoints of 62 expert engineers, contractors, consultants about the advantage and drawbacks of five more common PDSs have been collected via questionnaires and interviews. At the end, a comprehensive methodology has been presented to fine the optimal PDS for each Infrastructural project using Analytic Hierarchy Process (AHP)