ORCID Profile
0000-0003-2223-5292
Current Organisation
University of Adelaide
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: MDPI AG
Date: 12-09-2019
DOI: 10.3390/APP9183833
Abstract: The Singaporean government has made building information modeling (BIM) implementation mandatory in new building projects with gross floor areas over 5000 m2, but the implementation is still plagued with hindrances such as lacking project-wide collaboration. The purposes of this study are to identify critical factors hindering BIM implementation in Singapore’s construction industry, analyze their interrelationships, and identify strategies for reducing these hindrances. The results from a survey of 87 experts and five post-survey interviews in the Singaporean construction industry identified 21 critical hindrances, among which “need for all key stakeholders to be on board to exchange information” was ranked top. These hindrances were categorized into lack of collaboration and model integration (LCMI), lack of continuous involvement and capabilities (LCIC), and lack of executive vision and training (LEVT). LEVT and LCIC contributed to LCMI LEVT caused LCIC. The proposed framework implying the key hindrances and their corresponding managerial strategies can help practitioners identify specific adjustments to their BIM implementation activities, which enables to efficiently achieve enhanced BIM implementation. The hindrances identified in this study facilitate overseas BIM implementers to customize their own lists of hindrances.
Publisher: Elsevier BV
Date: 2023
Publisher: MDPI AG
Date: 19-07-2021
DOI: 10.3390/APP11146612
Abstract: This paper aims to critically review the current body of literature relating to the calculation methods of construction material stock. To this end, this study adopts a systematic literature review technique in order to identify the relevant studies. The findings revealed that the bottom-up and top-down methodologies were commonly employed by the reviewed studies. Based on the findings, it is recommended that the bottom-up approach should be utilized when dealing with small-scale areas or where more accurate results are required. The top-down method should be used wherein the research area is large, and the results could be estimated based upon assumptions and statistical data. Similarly, the demand-driven methodology should be used to find the material stock accumulation due to socio-economic factors. The study also found that the material stock results can be used as data for other research, such as waste management and embodied energy. Further, this paper proposes a conceptual framework to ease the process of calculating construction material stocks in different projects. The outcomes of this research shall be beneficial for future studies that explore the literature connected to the construction material stock and recommend methods and techniques that should be used to quantify the material stock.
Publisher: Elsevier BV
Date: 2023
Publisher: MDPI AG
Date: 25-02-2022
DOI: 10.3390/APP12052420
Abstract: The Australian Bureau of Statistics (ABS) regularly releases statistical information, for the whole of Australia, for public access. Building- and construction-related statistics are important to reflect the status of this pillar industry of Australia and help researchers, practitioners, and investors with decision-making. Due to complex retrieval hierarchy of ABS’s website and irregular update frequency, it is usually time-consuming to find relevant information. Moreover, browsing the raw data from ABS’s webpages could not provide the insights to the future. In this work, we applied techniques from computer science to help users in the building and construction domain to better explore the ABS statistics and forecast the future trends. Specifically, we built an integrated Web application that could help collect, sort, and visualize the ABS statistics in a user-friendly and customized way. Our Web application is publicly accessible. We further injected our insights into the Web application, based on the existing data by providing online forecasting on user’s interested information. To achieve this, we identified a series of related economic factors as features and adjusted a multi-variant, LSTM-based time series forecasting model by considering the most informative factors. We also compared our approach with the most widely used SARIMA-based forecasting model to show the effectiveness of the deep learning-based models. The forecast values are depicted at the end of the time series plots, selected by the users.
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 10-2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2016
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 04-2022
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 04-2023
Publisher: ACM
Date: 08-01-2021
Publisher: Elsevier BV
Date: 02-2013
Publisher: Elsevier BV
Date: 09-2016
Publisher: Wiley
Date: 06-01-2017
DOI: 10.1002/SD.1661
Publisher: Elsevier BV
Date: 08-2016
Publisher: Springer Science and Business Media LLC
Date: 04-07-2019
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 04-2020
Publisher: Springer Science and Business Media LLC
Date: 28-07-2022
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 05-2017
Publisher: Emerald
Date: 18-09-2017
DOI: 10.1108/ECAM-01-2016-0025
Abstract: Sustainability and competitiveness have received extensive attentions. Despite a large number of studies on sustainability and competitiveness in the construction industry, little research has been conducted to holistically explore the interactions between these two concepts. From a dynamic transition perspective, the purpose of this paper is to link sustainability and competitiveness of construction firms by developing a Sustainability-Competitiveness Dynamic Interaction Framework (SCDIF). Conceptual theory-building approach was adopted to develop the conceptual framework. It is an iterative analysis and synthesis process, which involves reading literature, identifying commonalities and differences, synthesizing, proposing an initial framework, collecting additional literature, and revisiting and revising the framework. There are complex interactions between sustainability and competitiveness of construction firms. This leads to uncertain relationships between sustainability and competitiveness, which is context dependent. Under evolving economic and socio-political environments, sustainability and competitiveness of construction firms could transition from mutually exclusive to mutually supportive, and finally merge into “sustainable competitiveness.” A SCDIF proposed in this study demonstrates that the interactions between sustainability and competitiveness evolves according to the evolving economic and socio-political environments and firms’ strategies, and thus the relationships and interactions between sustainability and competitiveness are context dependent. This framework helps corporate managers to understand how corporate sustainability and competitiveness interact with each other, thereby informing their decision-making of sustainability strategy. Similarly, the framework provides useful references for policymakers to understand the mechanisms of transitioning industries toward sustainable competitiveness. The proposed framework offers a new perspective for understanding sustainability and competitiveness. From the dynamic transition perspective, this study effectively illustrates that the interactions between sustainability and competitiveness evolves according to the evolving economic and socio-political environments and firms’ strategies. Compared to existing approaches, the dynamic and holistic approach proposed in this paper provides the capacity to capture the complexity of sustainability and competitiveness.
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2019
Publisher: Elsevier BV
Date: 02-2021
Publisher: Emerald
Date: 27-07-2023
DOI: 10.1108/SASBE-05-2023-0111
Abstract: This study aims to investigate the literature related to the use of digital technologies for promoting circular economy (CE) in the construction industry. A comprehensive approach was adopted, involving bibliometric analysis, text-mining analysis and content analysis to meet three objectives (1) to unveil the evolutionary progress of the field, (2) to identify the key research themes in the field and (3) to identify challenges hindering the implementation of digital technologies for CE. A total of 365 publications was analysed. The results revealed eight key digital technologies categorised into two main clusters including “digitalisation and advanced technologies” and “sustainable construction technologies”. The former involved technologies, namely machine learning, artificial intelligence, deep learning, big data analytics and object detection and computer vision that were used for (1) forecasting construction and demolition (C& D) waste generation, (2) waste identification and classification and (3) computer vision for waste management. The latter included technologies such as Internet of Things (IoT), blockchain and building information modelling (BIM) that help optimise resource use, enhance transparency and sustainability practices in the industry. Overall, these technologies show great potential for improving waste management and enabling CE in construction. This research employs a holistic approach to provide a status-quo understanding of the digital technologies that can be utilised to support the implementation of CE in construction. Further, this study underlines the key challenges associated with adopting digital technologies, whilst also offering opportunities for future improvement of the field.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 02-2019
Publisher: Research Square Platform LLC
Date: 27-04-2023
DOI: 10.21203/RS.3.RS-2815646/V1
Abstract: Australian residents have the highest solar energy installation rate in the world. However, after experiencing rapid growth, the residential installation rate began to decline sharply starting in 2011. In this study, we examine installation data for small and medium-sized solar photovoltaic (PV) devices across 2,413 Australian postcode areas using regression analysis and coarsened exact matching (CEM). We reevaluate four primary factors influencing the residential solar energy installation rate: natural factors, energy policy factors, rooftop space factors, and socioeconomic factors. Our findings reveal that areas with higher proportions of elderly or low-income residents exhibit greater household solar PV installation rates. Additionally, increased feed-in tariff (FiT) subsidies further motivate these resident groups to install solar PV systems, resulting in higher installation rates within their neighborhoods. The results also suggest that if a subsidy policy with a feed-in tariff average expected return (FiT-AER) of more than 7 cents/kWh persists, elderly and low-income groups in Australia may continue to dominate home solar PV installations. This trend could potentially mitigate energy inequity during the energy transition process.
Publisher: Elsevier BV
Date: 10-2023
Publisher: MDPI AG
Date: 18-08-2018
DOI: 10.3390/SU10082939
Abstract: Mega sustainable construction projects (MSCPs) require complex system engineering. There are various indicators available to evaluate sustainable construction, and it is difficult to determine which the key indicators are among them. Existing studies do not adequately consider the stakeholders associated with the indicators of sustainable construction, leading to key decision-makers’ lack of targeted management strategies to improve the sustainability level of MSCPs. Using literature analysis and expert interviews, this study identified the key evaluation indicators of MSCPs from a stakeholder-network perspective. Social network analysis (SNA) was used to explore the relationships between the key evaluation indicators and corresponding stakeholders. The results showed that the government and designers significantly impacted other stakeholders and played as the key stakeholders in MSCPs. Regarding the indicators, applying energy-saving and intelligent technologies plays a key role in the MSCPs. This study links key indicators of MSCPs with the associated stakeholders, which helps decision-makers to develop targeted strategies to improve the sustainability level of MSCPs, thereby not only improving the efficiency and effectiveness of the intervention strategies, but also helping to save decision-makers’ monetary and human resources which are usually limited.
Publisher: College Publishing
Date: 06-2018
DOI: 10.3992/1943-4618.13.3.122
Abstract: The building industry has experienced a widespread transition towards green buildings and consequently a growing need for green facilities professionals to maximize green building potential in terms of energy efficiency, water conservation and waste reduction in their operational stage. Green buildings have unique technological systems that require facility managers to have relevant knowledge and skills to conduct proper facilities management and maintenance planning to maximize the potential of green buildings. It is important, then, to investigate whether knowledge gaps for facility managers exist with respect to green buildings, and if so, how these knowledge gaps could be bridged. Though several studies have investigated the operation and maintenance processes of green buildings, few studies considered facility managers' knowledge and skills regarding green facility management (GFM). Set in the context of Singapore, this study aims to holistically investigate the knowledge and skills of managing green buildings in the community of facility managers, including their perceived differences between green and conventional buildings, the difficulty of GFM, the knowledge gaps of GFM and the underlying reasons, as well as how the gaps could be bridged. A total of 90 survey responses were collected and eight interviews with key stakeholders were conducted, which indicate facility managers believe green buildings do have special features that require unique knowledge and skills, and currently knowledge gaps do exist hindering the transition towards GFM. Therefore, this paper derives plausible solutions to bridge the knowledge gaps, such as establishing holistic subsidies for those facility managers participating in training programs of GFM. This study provides references for researchers and relevant governmental departments to better understand industry professionals' knowledge gaps in the transitioning process towards a green built environment, and to make better policy decisions bridging the knowledge gaps and thereby facilitating the green transition process.
Publisher: MDPI AG
Date: 04-07-2022
DOI: 10.3390/BUILDINGS12070952
Abstract: As a powerful artificial intelligence tool, the Artificial Neural Network (ANN) has been increasingly applied in the field of construction management (CM) during the last few decades. However, few papers have attempted to draw up a systematic commentary to appraise the state-of-the-art research on ANNs in CM except the one published in 2000. In the present study, a scientometric analysis was conducted to comprehensively analyze 112 related articles retrieved from seven selected authoritative journals published between 2000 and 2020. The analysis identified co-authorship networks, collaboration networks of countries/regions, co-occurrence networks of keywords, and timeline visualization of keywords, together with the strongest citation burst, the active research authors, countries/regions, and main research interests, as well as their evolution trends and collaborative relationships in the past 20 years. This paper finds that there is still a lack of systematic research and sufficient attention to the application of ANNs in CM. Furthermore, ANN applications still face many challenges such as data collection, cleaning and storage, the collaboration of different stakeholders, researchers and countries/regions, as well as the systematic design for the needed platforms. The findings are valuable to both the researchers and industry practitioners who are committed to ANNs in CM.
Publisher: Elsevier BV
Date: 06-0003
Publisher: Elsevier BV
Date: 08-2023
Publisher: No publisher found
Date: 2019
Publisher: Emerald
Date: 19-01-2022
DOI: 10.1108/ECAM-06-2021-0538
Abstract: Prefabricated construction (PC) will play a vital role in the transformation and upgrading of the construction industry in the future. However, high capital cost is currently one of the biggest obstacles to the application and promotion of PC in China. Clarifying the factors that affect the PC cost from the perspectives of stakeholders and exploring key cost control paths help to achieve effective cost management, but few studies have paid enough attention to this. Therefore, this research aims to explore the critical cost influencing factors (CIFs) and critical stakeholders of PC based on stakeholder theories and propose corresponding strategies for different stakeholders to reduce the cost of PC. Based on the stakeholder theory and social network theory, literature review and two rounds of expert interviews were used to obtain the stakeholder-associated CIFs and their mutual effects, then the consistency of the data was tested. After that, social network analysis was applied to identify the critical CIFs, critical interaction and key stakeholders in PC cost control and mine the influence conduction paths between CIFs. The results reveal that the cognition and attitude of developer and relevant standards and codes are the most critical CIFs while the government, developer and contractor are crucial to the cost control of PC. The findings further suggest that measures should be taken to reduce the transaction costs of the developer, and the contractor ought to efficiently apply information technology. Moreover, the collaborative work between designer and manufacturer can avoid unnecessary cost consumption. This research combines stakeholder management and cost management in PC for the first time and explores the effective cost control paths. The research results can contribute to clarifying the key points of cost management for different stakeholders and improving the cost performance of PC projects.
Publisher: MDPI AG
Date: 15-06-2021
DOI: 10.3390/APP11125531
Abstract: Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance.
Publisher: MDPI AG
Date: 15-10-2021
DOI: 10.3390/APP11209616
Abstract: Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future.
Publisher: Elsevier BV
Date: 10-2018
Publisher: Emerald
Date: 28-02-2019
DOI: 10.1108/ECAM-05-2018-0198
Abstract: The purpose of this paper is to identify the interactions of factors impacting the widespread adoption of prefabricated building technologies and the intervention strategies to facilitate the development of prefabrication based on fuzzy cognitive maps (FCMs). Through in-depth interviews with six stakeholder groups, namely, the government, developers, designers, contractors, manufacturers and researchers, 13 critical factors were identified and used to construct stakeholder-grouped FCMs, which were further aggregated into a collective FCM. The complexity and density of the collective FCM and the centrality of factors in the FCM were examined. Subsequently, a series of “what-if” simulations of the collective FCM were conducted to analyze the effectiveness of different interventions in promoting prefabrication. The results show that three factors including market demand, cost, and policies and regulations have been mentioned by all stakeholder groups. However, these factors were ranked differently by stakeholder groups, implying that different stakeholder groups perceive the barriers to prefabricated building technologies differently. FCM simulations show that strengthening policies and regulations yield the strongest overall effect stimulating prefabrication, alleviating the organizational and environmental barriers more than the technological barriers, while improving the knowledge and expertise alleviate the technological barriers more. These measures need to be accompanied by other approaches, such as reducing cost and improving quality. It is a tough task to promote prefabrication as it is affected by numerous barriers with complex interactions, which have been overlooked by previous studies. This study clearly shows which strategy could tackle which barriers to prefabrication through the FCM simulations. This provides valuable references for the enterprises’ decision making and the governments’ policy making to facilitate the diffusion of prefabricated building technologies. Few studies aim to analyze the interactions among the barriers to prefabrication, while this study specifically investigates this issue by illustrating the complex interactions using FCMs. Few studies also aim to identify the intervention strategies promoting prefabrication based on a quantitative approach, while this study employs FCM simulations to directly simulate the effectiveness of different strategies to facilitate prefabrication in a quantitative manner.
Publisher: MDPI AG
Date: 02-01-2020
DOI: 10.3390/SU12010355
Abstract: Building information modeling (BIM) is deemed a useful innovation for technological and sustainable development of the economy. It is partially used in building projects in Singapore, although its implementation is mandated by the local government, resulting in various wastes and suboptimal productivity. Little is known about how non-value adding (NVA) BIM implementation practices were perceived by the local practitioners and how these practices affected productivity in building projects in Singapore. This study aimed to identify critical NVA BIM implementation activities and investigate the criticality of their resulting wastes to productivity performance in the current project delivery process in Singapore. The results from a questionnaire survey of 73 experts and four post-survey interviews in Singapore revealed that 38 NVA BIM implementation activities were deemed critical, among which “lack of involvement by contractors to contribute site knowledge” in the design development phase was ranked top the top five resulting wastes with highest criticalities were reworks/abortive works, requests for information, design deficiencies, defects, and waiting/idle time. Furthermore, an independent-s les t-test was conducted to examine whether construction firms and upfront stakeholders perceived the NVA activities differently. It was discovered that most NVA activities exerted more agreement from construction firms than upfront non-construction organizations. Six strategies were proposed to mitigate the NVA activities and wastes. The findings can help practitioners identify weak areas of their BIM implementation practices and prioritize resources accordingly to eliminate the wastes and foster sustainability, as well as help overseas project teams, with minor adjustments, customize their own NVA BIM implementation activities and management strategies.
Publisher: MDPI AG
Date: 06-07-2108
DOI: 10.3390/APP10238680
Abstract: Quality control is essential to a successful modular construction project and should be enhanced throughout the project from design to construction and installation. The current methods for analyzing the assembly quality of a removable floodwall heavily rely on manual inspection and contact-type measurements, which are time-consuming and costly. This study presents a systematic and practical approach to improve quality control of the prefabricated modular construction projects by integrating building information modeling (BIM) with three-dimensional (3D) laser scanning technology. The study starts with a thorough literature review of current quality control methods in modular construction. Firstly, the critical quality control procedure for the modular construction structure and components should be identified. Secondly, the dimensions of the structure and components in a BIM model is considered as quality tolerance control benchmarking. Thirdly, the point cloud data is captured with 3D laser scanning, which is used to create the as-built model for the constructed structure. Fourthly, data analysis and field validation are carried out by matching the point cloud data with the as-built model and the BIM model. Finally, the study employs the data of a removable floodwall project to validate the level of technical feasibility and accuracy of the presented methods. This method improved the efficiency and accuracy of modular construction quality control. It established a preliminary foundation for using BIM and laser scanning to conduct quality control in removable floodwall installation. The results indicated that the proposed integration of BIM and 3D laser scanning has great potential to improve the quality control of a modular construction project.
Publisher: Emerald
Date: 21-12-2021
DOI: 10.1108/ECAM-10-2021-0895
Abstract: Building information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase. An SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation. The simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM. This study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.
Publisher: Elsevier BV
Date: 09-2023
Publisher: MDPI AG
Date: 18-06-2020
DOI: 10.3390/APP10124188
Abstract: Environmental concerns and growing energy costs raise the importance of sustainable development and energy conservation. The building sector accounts for a significant portion of total energy consumption. Passive cooling techniques provide a promising and cost-efficient solution to reducing the energy demand of buildings. Based on a typical residential case in Hong Kong, this study aims to analyze the integration of various passive cooling techniques on annual and hourly building energy demand with whole building simulation. The results indicate that infiltration and insulation improvement are effective in regard to energy conservation in buildings, while the effectiveness of variations in building orientation, increasing natural ventilation rate, and phase change materials (PCM) are less significant. The findings will be helpful in the passive house standard development in Hong Kong and contribute to the further optimization work to realize both energy efficiency and favorably built environments in residential buildings.
Publisher: Vilnius Gediminas Technical University
Date: 06-01-2022
Abstract: The performance-based payment PPP model has been widely used in the infrastructure projects. However, the ratchet effect derived from performance-based reputation incentives has been largely overlooked. To overcome this shortcoming, ratchet effect is considered in the performance-based payment incentive process. A multi-period dynamic incentive mechanism is developed by coupling the reputation and ratchet effect. The main results show that: (1) Under the coupling of reputation and ratchet effects, the optimal incentive coefficient in the last performance assessment period is always greater than that of the first period. The bargaining power can replace part of the incentive effect (2) Due to the ratchet effect, if the government improve performance targets through performance adjustment coefficients, it needs to increase incentives to overcome the decreasing effort of the private sector (3) When the bargaining power and punishment coefficient are small, the reputation incentive is replacing the explicit incentive. The increasing incentive coefficient would make the ratchet effect dominant the reputation effect (4) To prevent the incentive incompatibility derived from the ratchet effect, the government should increase the incentive while increasing the punishment to achieve the “penalties and rewards”. This study provides theoretical and methodological guidance to design incentive contracts for infrastructure PPP projects.
Publisher: Informa UK Limited
Date: 30-03-2020
Publisher: Elsevier BV
Date: 11-2018
Publisher: College Publishing
Date: 09-2022
DOI: 10.3992/JGB.17.4.99
Abstract: As the largest construction industry in the world, the Chinese construction industry not only has huge sustainability implications for China, but also significantly influences the world’s resource consumption due to its tremendous scale. However, there is a lack of studies identifying the fundamental challenges hindering the transformation towards sustainable construction in China from a holistic triple bottom line perspective of sustainability. This study aims to identify the challenges, thereby revealing the future research directions accelerating the transition towards sustainable construction in China. Through content analysis of both existing literature and government plans, this study identifies the key sustainability challenges, governmental measures, and critical future research opportunities to help tackle the challenges hindering the sustainability transition of the Chinese construction industry. Even though this study focuses on China, it provides a holistic reference for researchers, industry practitioners and policymakers worldwide to understand the sustainability challenges of the construction industry, as the construction industries in many countries face sustainability challenges similar to those in China.
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 03-2023
Publisher: American Society of Civil Engineers
Date: 09-11-2020
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2019
Publisher: Elsevier BV
Date: 06-2019
Publisher: American Society of Civil Engineers (ASCE)
Date: 10-2023
Publisher: Emerald
Date: 28-02-2023
DOI: 10.1108/ECAM-04-2022-0345
Abstract: Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling. The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems. In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation. This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 07-2016
Publisher: Elsevier BV
Date: 11-2016
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2017
Publisher: Elsevier BV
Date: 09-2017
Publisher: MDPI AG
Date: 20-09-2020
DOI: 10.3390/SU12187762
Abstract: Building information modeling (BIM) implementation has been mandated in building projects in Singapore, but a wider adoption is still desired. This study aims to investigate the factors influencing BIM diffusion and examine how the factors influence firms with different project roles, firm sizes, and BIM implementation experience. The results of a pilot study, a questionnaire survey with 89 professionals, and five post-survey interviews showed that hindrances related to inadequate multi-party collaboration (whether formal or informal), conservative mindset, limited skills, costly infrastructure and training, and multi-discipline model integration were the most influential, whereas drivers associated with project leadership team’s strategic consensus, multi-disciplinary design coordination, training, and government regulations were top-ranked. Subgroup analyses between pairs of firms with different characteristics revealed that while construction firms and less experienced stakeholders tended to underestimate BIM implementation difficulties, small-medium contractors might underestimate relevant benefits. The findings and managerial recommendations help different types of firms prioritize resources to overcome hindrances, seize opportunities (such as gaining a competitive edge from BIM practical experience), and obtain support from workers executing BIM daily. With major stakeholders’ recognition and implementation, BIM can be successfully diffused in building projects and firms. The Singapore government and other countries can refer to this study when further issuing BIM diffusion policies.
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/1347914
Abstract: Considering the effects of the contractor’s conflict behaviors on the project benefit, a decision model between the owner and contractor’s conflict behaviors in construction projects was constructed using the principal-agent theory and game theory. The model was analyzed under nonconflicting and conflicting conditions, and a numerical simulation and ex le analysis were proposed to verify the constructed model’s conclusion. The results showed that the effort levels of the owner and contractor not only relate to benefit-sharing coefficient and effort outcome coefficient but also depend on the contractor’s ability of converting the conflict into benefit and the loss caused by conflict behaviors. A higher ability of converting conflicts into benefits and lower levels of the loss caused by conflict behaviors for the contractor lead to lower levels of the net benefit of the owner, conversely higher levels of the net benefit of the contractor. Balancing the contractor’s ability of converting conflicts into benefits and the loss caused by conflict behaviors lead to a more reasonable risk allocation between the owner and contractor, improving the effort level and net benefit. To add value to the construction project, the owner should establish an impartial and reasonable benefit-sharing mechanism, optimize the owner and contractor’s resource arrangement, maximize the positive effect of conflict on project benefits, and avoid the negative effect of conflict. Few studies to date have investigated the effects of conflict behaviors on project benefits in terms of modeling and simulation in construction projects. As such, this study bridges this gap and contributes significant theoretical and practical insights about managing conflict behaviors in an interorganizational context, thus enhancing performance in construction projects.
Publisher: Elsevier BV
Date: 05-2022
Publisher: MDPI AG
Date: 28-05-2021
DOI: 10.3390/BUILDINGS11060230
Abstract: This paper aims to propose a comprehensive framework for a clear description of system boundary conditions in life cycle energy assessment (LCEA) analysis in order to promote the incorporation of embodied energy impacts into building energy-efficiency regulations (BEERs). The proposed framework was developed based on an extensive review of 66 studies representing 243 case studies in over 15 countries. The framework consists of six distinctive dimensions, i.e., temporal, physical, methodological, hypothetical, spatial, and functional. These dimensions encapsulate 15 components collectively. The proposed framework possesses two key characteristics first, its application facilitates defining the conditions of a system boundary within a transparent context. This consequently leads to increasing reliability of obtained LCEA results for decision-making purposes since any particular conditions (e.g., truncation or assumption) considered in establishing the boundaries of a system under study can be revealed. Second, the use of a framework can also provide a meaningful basis for cross comparing cases within a global context. This characteristic can further result in identifying best practices for the design of buildings with low life cycle energy use performance. Furthermore, this paper applies the proposed framework to analyse the LCEA performance of a case study in Adelaide, Australia. Thereafter, the framework is utilised to cross compare the achieved LCEA results with a case study retrieved from literature in order to demonstrate the framework’s capacity for cross comparison. The results indicate the capability of the framework for maintaining transparency in establishing a system boundary in an LCEA analysis, as well as a standardised basis for cross comparing cases. This study also offers recommendations for policy makers in the building sector to incorporate embodied energy into BEERs.
Publisher: Emerald
Date: 24-01-2023
DOI: 10.1108/ECAM-07-2022-0666
Abstract: Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available. The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China. The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors. (1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 02-2023
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2020
No related grants have been discovered for Ruidong Chang.