ORCID Profile
0000-0003-4148-3160
Current Organisation
Monash University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Civil Engineering | Construction Engineering | Structural Engineering | Construction Materials | Civil Geotechnical Engineering | Infrastructure Engineering and Asset Management |
ANZSRC Socio-Economic Objective (SEO)
Metals (e.g. Composites, Coatings, Bonding) | Civil Construction Processes | Civil Construction Design | Road Freight | Road Infrastructure and Networks | Management of Greenhouse Gas Emissions from Construction Activities
Related Links
Publications
An artificial intelligence framework for predicting fire spread sustainability in semiarid shrublands
Publisher: CSIRO Publishing
Date: 03-02-2023
DOI: 10.1071/WF22216
Abstract: Background Fire behaviour simulation and prediction play a key role in supporting wildfire management and suppression activities. Aims Using machine-learning methods, the aim of this study was to predict the onset of fire propagation (go vs no-go) and type of fire behaviour (surface vs crown fire) in southern Australian semiarid shrublands. Methods Several machine-learning (ML) approaches were tested, including Support Vector Machine, Multinomial Naive Bayes and Multilayered Neural Networks, as was the use of augmented datasets developed with Generative Adversarial Networks (GAN) in classification of fire type. Key results Support Vector Machine was determined as the optimum machine learning classifier based on model overall accuracy against an independent evaluation dataset. This classifier correctly predicted fire spread sustainability and active crown fire propagation in 70 and 79% of the cases, respectively. The application of synthetically generated datasets in the Support Vector Machine model fitting process resulted in an improvement of model accuracy by 20% for the fire sustainability classification and 4% for the crown fire occurrence. Conclusions The selected ML modelling approach was shown to produce better results than logistic regression models when tested on independent datasets. Implications Artificial intelligence frameworks have a role in the development of predictive models of fire behaviour.
Group Dynamics in Higher Education
Publisher: IGI Global
Date: 2020
DOI: 10.4018/978-1-5225-8452-0.CH003
Abstract: Although group work has been proven to be an effective method for enhancing active learning in the higher education, optimum planning is crucial for successful implementation. A deep understanding of teamwork dynamics and creation of inclusive environments helps groups to demonstrate their optimum performance and output. On this basis, the current research focuses on the important challenge of gender inclusiveness and required teacher interventions to encourage that. Towards this aim, three research hypotheses are developed and tested using student performance data in a series of in idual, group, and hybrid assessment. Findings show the significantly different performance of female and male students in group activities. It is also found that instructor interventions to form gender-inclusive groups significantly improve group performance and output. This works contributes to the higher education literature by exploring dynamics of collaborative learning and interfaces with gender inclusiveness. Educators can utilize the findings to better design and implement team activities.
Multi-modal mining of crowd-sourced data: Efficient provision of humanitarian aid to remote regions affected by natural disasters
Publisher: Elsevier BV
Date: 10-2023
Multiskilled Human Resource Problem in Off-Site Construction
Publisher: International Association for Automation and Robotics in Construction (IAARC)
Date: 22-07-2018
Prediction of rheological properties and ageing performance of recycled plastic modified bitumen using Machine learning models
Publisher: Elsevier BV
Date: 10-2023
Viability of the BIM Manager Enduring as a Distinct Role: Association Rule Mining of Job Advertisements
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2018
Multiskilled Workforce Planning: A Case from the Construction Industry
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2022
Hybrid bi-objective economic lot scheduling problem with feasible production plan equipped with an efficient adjunct search technique
Publisher: Informa UK Limited
Date: 09-04-2022
Bond-slip performance of seawater sea sand concrete filled filament wound FRP tubes under cyclic and static loads
Publisher: Elsevier BV
Date: 06-2023
Enhancing construction safety: Machine learning-based classification of injury types
Publisher: Elsevier BV
Date: 06-2023
Framework for improving workflow stability: deployment of optimized capacity buffers in a synchronized construction production
Publisher: Canadian Science Publishing
Date: 12-2014
Abstract: Construction sites are dynamic environments due to the influence of variables such as changes in design and processes, unsteady demand, and unavailability of trades. These variables adversely affect productivity and can cause an unstable workflow in the network of trade contractors. Previous research on workflow stability in the construction and manufacturing domains has shown the effectiveness of ‘pull’ production or ‘rate driven’ construction. Pull systems authorize the start of construction when a job is completed and leaves the trade contractor network. However, the problem with pull systems is that completion dates are not explicitly considered and therefore additional mechanisms are required to ensure the due date integrity. On this basis, the aim of this investigation is to improve the coordination between output and demand using optimal-sized capacity buffers. Towards this aim, production data of two Australian construction companies were collected and analyzed. Capacity and cost optimizations were conducted to find the optimum buffer that strikes the balance between late completion costs and lost revenue opportunity. Following this, simulation experiments were designed and run to analyze different ‘what-if’ production scenarios. The findings show that capacity buffers enable builders to ensure a desired service level. Size of the capacity buffer is more sensitive to the level of variability in contractor processes than other production variables. This work contributes to the body-of-knowledge by improving production control in construction and deployment of capacity buffers to achieve a stable workflow. In addition, construction companies can use the easy-to-use framework tested in this study to compute the optimal size for capacity buffers that maximizes profit and prevents late completions.
Enhancing collaboration in BIM-based construction networks through organisational discontinuity theory: a case study of the new Royal Adelaide Hospital
Publisher: Informa UK Limited
Date: 06-04-2016
Sustainability by Information and Communication Technology: A paradigm shift for construction projects in Iran
Publisher: Elsevier BV
Date: 12-2017
Analysis of interacting uncertainties in on-site and off-site activities: Implications for hybrid construction
Publisher: Elsevier BV
Date: 2016
Green mix design of rubbercrete using machine learning-based ensemble model and constrained multi-objective optimization
Publisher: Elsevier BV
Date: 12-2021
Residual compressive strength of seawater sea sand concrete filled hybrid carbon-glass fibre reinforced polymer tubes under seawater: Effects of fibre type and orientation
Publisher: Elsevier BV
Date: 07-2023
Evaluating the Impact of Building Information Modeling on the Labor Productivity of Construction Projects in Malaysia
Publisher: MDPI AG
Date: 30-03-2020
DOI: 10.3390/BUILDINGS10040066
Abstract: Globally, the construction sector suffers from low productivity levels due to a large proportion of the workforce consisting of low-skilled laborers. There is a significant need to move from traditional approaches to advanced methods, such as Building Information Modeling, in order to integrate design and construction workflows with the aim of improving productivity. To encourage more organizations, especially small to medium enterprises (SME), to transition to building information modeling (BIM), clear and convincing benefits are key to ensuring the viability of the BIM implementation process. This study presents the findings obtained through a quantitative structured close-ended survey questionnaire distributed among BIM-pioneering construction companies in terms of the three factors of the project, organization, and in idual. The results suggest that BIM factors related to the in idual supervision category have the highest positive impact, while the In idual (Labor) factor has the most negative impact on labor productivity. The study concludes by recommending the incorporation of BIM in the In idual (Supervision) category to improve the low construction productivity. A practical recommendation for building regulatory bodies is to develop comprehensive credential training programs with the greater utilization of BIM-related design and construction management to diminish the negative impact of In idual (Labor) factors and thus improve labor productivity in the construction sector.
Sustainable pavement construction: A systematic literature review of environmental and economic analysis of recycled materials
Publisher: Elsevier BV
Date: 09-2021
Application of Nonlinear-Autoregressive-Exogenous model to predict the hysteretic behaviour of passive control systems
Publisher: Elsevier BV
Date: 02-2015
Lean design management using a gamified system
Publisher: SciTech Solutions
Date: 25-04-2018
Analysis of Disruptions Caused by Construction Field Rework on Productivity in Residential Projects
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2014
Crane payload localisation for curtain wall installation: A markerless computer vision approach
Publisher: Elsevier BV
Date: 11-2023
Comparative life cycle assessment of reprocessed plastics and commercial polymer modified asphalts
Publisher: Elsevier BV
Date: 02-2022
Recursive principal component analysis for model order reduction with application in nonlinear Bayesian filtering
Publisher: Elsevier BV
Date: 11-2020
Investigating the Barriers to Applying the Internet-of-Things-Based Technologies to Construction Site Safety Management
Publisher: MDPI AG
Date: 13-01-2022
Abstract: The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to “productivity reduction due to wearable sensors”, “the need for technical training”, and “the need for continuous monitoring” were the most significant, while “limitations on hardware and software and lack of standardization in efforts,” “the need for proper light for smooth functionality”, and “safety hazards” were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM.
Design for Manufacture and Assembly in Off-Site Construction: Advanced Production of Modular Façade Systems
Publisher: International Association for Automation and Robotics in Construction (IAARC)
Date: 22-07-2018
Skill Set Configuration in Prefabricated Construction: Hybrid Optimization and Multicriteria Decision-Making Approach
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2019
Quantitative Analysis of Safety Risks and Relationship with Delayed Project Completion Times
Publisher: Wiley
Date: 23-06-2021
DOI: 10.1111/RISA.13778
Abstract: Dynamic work environments in construction and civil infrastructure sectors remain susceptible to safety risks. Although previous research has resulted in improvements, there is currently a gap in measuring temporal impacts of safety risks quantitatively. Precise modeling of potential delays caused by safety incidents is vital for efficient management of risks and making informed decisions on project contingency. Toward this aim, the current research adopts a nondeterministic modeling method to simulate and quantify safety incidents and find correlations with project delays. Using a deductive approach, three research questions were formulated, and investigations conducted on Australian data collected from 2016 onwards. Quantitative impacts of safety risks on project completion times were numerically measured. Furthermore, safety risks were ranked based on their significance of temporal impacts on project performance. This paper contributes to the theory of safety management by developing a nondeterministic method to model impacts of safety risks at both industry and project levels. Practical contributions and outcomes can facilitate using machine learning methods to plan proportionate time buffers to address safety risks.
Behavior and design of thin-walled double-skin concrete-filled rectangular steel tubular short and slender columns with external stainless-steel tube incorporating local buckling effects
Publisher: Elsevier BV
Date: 2022
Quantitative analysis of rate-driven and due date-driven construction: Production efficiency, supervision, and controllability in residential projects
Publisher: American Society of Civil Engineers (ASCE)
Date: 2016
AI explainability framework for environmental management research
Publisher: Elsevier BV
Date: 09-2023
Prediction of wildfire rate of spread in grasslands using machine learning methods
Publisher: Elsevier BV
Date: 10-2022
Optimization of process integration and multi-skilled resource utilization in off-site construction
Publisher: Elsevier BV
Date: 02-2015
Off-site construction optimization: Sequencing multiple job classes with time constraints
Publisher: Elsevier BV
Date: 11-2016
Multi-objective optimization of shading devices using ensemble machine learning and orthogonal design of experiments
Publisher: Elsevier BV
Date: 03-2023
Predicting individual learning performance using machine‐learning hybridized with the teaching‐learning‐based optimization
Publisher: Wiley
Date: 29-09-2022
DOI: 10.1002/CAE.22572
Abstract: Reliable prediction of in idual learning performance can facilitate timely support to students and improve the learning experience. In this study, two well‐known machine‐learning techniques, that is, support vector machine (SVM) and artificial neural network (ANN), are hybridized by teaching–learning‐based optimizer (TLBO) to reliably predict the student exam performance (fail‐pass classes and final exam scores). For the defined classification and regression problems, the TLBO algorithm carries out the feature selection process of both ANN and SVM techniques in which the optimal combination of the input variables is determined. Moreover, the ANN architecture is determined using the TLBO algorithm parallel to the feature selection process. Finally, four hybrid models containing anonymized information on both discrete and continuous variables were developed using a comprehensive data set for learning analytics. This study provides scientific utility by developing hybridized machine‐learning models and TLBO, which can improve the predictions of student exam performance. In practice, in idual performance prediction can help to advise students about their academic progress and to take appropriate actions such as dropping units in subsequent teaching periods. It can also help scholarship providers to monitor student progress and provision of support.
Sensor-based safety management
Publisher: Elsevier BV
Date: 05-2020
Integrated management of on-site, coordination and off-site uncertainty: Theorizing risk analysis within a hybrid project setting
Publisher: Elsevier BV
Date: 05-2017
Back-stepping control of delta parallel robots with smart dynamic model selection for construction applications
Publisher: Elsevier BV
Date: 05-2022
Optimal Work Assignment to Multiskilled Resources in Prefabricated Construction
Publisher: American Society of Civil Engineers (ASCE)
Date: 04-2019
Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings
Publisher: Elsevier BV
Date: 04-2022
Scene understanding in construction and buildings using image processing methods: A comprehensive review and a case study
Publisher: Elsevier BV
Date: 2021
Efficiency of VR-Based Safety Training for Construction Equipment: Hazard Recognition in Heavy Machinery Operations
Publisher: MDPI AG
Date: 28-11-2022
DOI: 10.3390/BUILDINGS12122084
Abstract: Machinery operations on construction sites result in many serious injuries and fatalities. Practical training in a virtual environment is the key to improving the safety performance of machinery operators on construction sites. However, there is limited research focusing on factors responsible for the efficiency of virtual training in increasing hazard identification ability among novice trainees. This study analyzes the efficiency of virtual safety training with head-mounted VR displays against flat screen displays among novice operators. A cohort of tower crane operation trainees was subjected to multiple simulations in a virtual towards this aim. During the simulations, feedback was collected using a joystick to record the accuracy of hazard identification while a post-simulation questionnaire was used to collect responses regarding factors responsible for effective virtual training. Questionnaire responses were analyzed using interval type-2 fuzzy analytical hierarchical process to interpret the effect of display types on training efficiency while joystick response times were statistically analyzed to understand the effect of display types on the accuracy of identification across different types of safety hazards. It was observed that VR headsets increase the efficiency of virtual safety training by providing greater immersion, realism and depth perception while increasing the accuracy of hazard identification for critical hazards such as electric cables.
The Cost Burden of Safety Risk Incidents on Construction: A Probabilistic Quantification Method
Publisher: Wiley
Date: 27-11-2022
DOI: 10.1111/RISA.13865
Abstract: The construction sector is vulnerable to safety risk incidents due to its dynamic nature. Although numerous research efforts and technological advancements have focused on addressing workplace injuries, most of the studies perform empirical and deterministic postimpact evaluations on construction project performance. The effective modeling of the safety risk impacts on project performance provides decisionmakers with a valuable tool toward incidents prevention and proper safety risk management. Therefore, this study collected Australian incident records from the construction industry from 2016 onwards and conducted discrete event simulation to quantitatively measure the impact of safety risk incidents on project cost performance. Moreover, this study investigated the correlation between safety risk incidents and the age of injured workers. The findings show a strong correlation between the middle‐aged workforce and the severity of incidents on project cost overruns. The ex‐ant e, nondeterministic analysis of safety risk impacts on project performance provides insightful results that will advance safety management theory in the direction of achieving zero harm workplace environments.
The evolution of stakeholder management practices in Australian mega construction projects
Publisher: Emerald
Date: 16-07-2018
DOI: 10.1108/ECAM-07-2016-0168
Abstract: The complicated nature of megaprojects requires appropriate analysis of multiple stakeholders to achieve project objectives and to accommodate stakeholder interests. During the last two decades, many stakeholder theories and empirical studies have sprouted. Although previous studies have contributed to the development of stakeholder theory, it seems that these theoretical advances have not been fully adopted and acknowledged in practices, especially in megaprojects. The purpose of this paper is to explore the evolution of stakeholder analysis and engagement practices adopted in the Australian megaprojects over the last two decades. Four mega construction projects are described and analysed in this study. Secondary data were first assembled in order to get general knowledge of each case. Interviews were conducted with the project directors. Project documents were collected from the project teams and reviewed. Wherever the project information was unclear, e-mails were sent to the directors and the team members to confirm the details. Project teams have started to apply snowball rolling and stakeholder attribute assessment methods to analyse stakeholders. However, there is still a way to adopt the “network” analysis perspective because the project teams are reluctant to use complicated tools which need specialists’ assistance. The stakeholder engagement practices have evolved to an extent where the project teams monitor the dynamics of stakeholders’ requirements. Projects teams have identified the importance of continuity to manage stakeholders in these massive projects. However, a structured method selection mechanism for stakeholder engagement has not been developed. This study will help academics to understand the adoption progress and status of stakeholder management methods.
Novel metaheuristic-based type-2 fuzzy inference system for predicting the compressive strength of recycled aggregate concrete
Publisher: Elsevier BV
Date: 10-2021
Enhancing Safety Training Performance Using Extended Reality: A Hybrid Delphi–AHP Multi-Attribute Analysis in a Type-2 Fuzzy Environment
Publisher: MDPI AG
Date: 26-02-2023
DOI: 10.3390/BUILDINGS13030625
Abstract: Safety training effectively addresses the inexperience of and lack of knowledge among construction workers, which are some of the most significant contributors to workplace accidents on construction sites. This paper aims to understand the effectiveness of different extended reality (XR) technologies in imparting important construction safety training to construction workers in a virtual environment compared to conventional classroom training sessions. A group of experts were engaged to understand the most effective learning criteria and the impact of XR visualizations, and their responses were analysed using the interval type-2 fuzzy Delphi (IT2FD) method. Following this, a cohort of engineering students were subjected to construction safety training in traditional, augmented reality (AR) and virtual reality (VR) environments. Their feedback was collected using an online questionnaire and the responses were analysed using the interval type-2 fuzzy analytic hierarchy process (IT2F–AHP). The results revealed that addressing the virtual interface design of the training to maintain the attention of trainees and ensuring the virtual environment’s resemblance to the actual site and task were the most important factors in ensuring effective knowledge retention by the trainees. AR visualizations were most effective at imparting knowledge, and their interactive nature allowed trainees to retain the learned knowledge.
Performance-based control of variability and tolerance in off-site manufacture and assembly: optimization of penalty on poor production quality
Publisher: Informa UK Limited
Date: 05-06-2019
Excavator 3D pose estimation using deep learning and hybrid datasets
Publisher: Elsevier BV
Date: 2023
Design for manufacture and assembly in offsite construction and relationship with concurrent engineering
Publisher: Routledge
Date: 19-06-2019
Modelling the relationship between digital twins implementation barriers and sustainability pillars: Insights from building and construction sector
Publisher: Elsevier BV
Date: 12-2023
An integrated model for factors affecting construction and demolition waste management in Iran
Publisher: Emerald
Date: 20-11-2017
DOI: 10.1108/ECAM-01-2016-0015
Abstract: Factors influencing management of construction and demolition (C& D) waste within the Iranian context have yet to be investigated. The purpose of this paper is to define and address this knowledge gap, through development of a model to map the associations among the primary factors affecting C& D waste at project, industry and national levels. A conceptual model is developed based on synthesising the findings of available studies on factors affecting C& D waste with a focus on developing countries. For collecting data, the study drew upon a questionnaire survey of 103 Iranian construction practitioners. The strength and significance of associations among these factors to modify and validate the model were assessed using the structural equation modelling-partial least squares approach. Major factors affecting C& D waste management and their level of importance were identified at project, industry and national levels. Results clearly showed that the government should review regulations pertaining to C& D waste management and make sure they are implemented properly. The “polluter pays principle” is a useful guide in devising effective policies and regulations for the Iranian context. This study contributes to the field through presenting the first major study on C& D waste management in Iran. The study provides a picture of C& D waste management status quo in Iran and encapsulates the factors affecting C& D waste management in the Iranian context at different levels within an integrated model. The findings have practical implications for policy makers and construction practitioners in Iran, similar developing economies and foreign firms planning to operate in Iran.
Dynamical modelling of boom tower crane rigging systems: model selection for construction
Publisher: Springer Science and Business Media LLC
Date: 05-06-2023
DOI: 10.1007/S43452-023-00702-X
Abstract: Accurate dynamical models are imperative to the development of accurate monitoring and control systems, which are foundational to safety in construction and infrastructure projects. However, the highly coupled non-linear dynamics of crane systems requires the application of many simplifying assumptions to the dynamical crane model. To achieve accurate control, simplifications should yield minimal error in modelled behaviour for maximal reduction in model complexity. However, limited information is available on the situational suitability of different combinations of simplifications to construction tower crane models. This paper informs designers of the optimal dynamical models to represent boom tower cranes, with respect to the crane characteristics and selection criteria. The optimal models are determined though the comparison of ten 2D and 3D dynamical models in representation of three variations of boom tower crane that are commonly deployed on construction sites. The comparison includes analysis of over 100 simulations and experimentation. The value of the presented optimal model selection framework is in facilitating systems designers to develop accurate crane monitoring and control systems.
Automatic generation of structural geometric digital twins from point clouds
Publisher: Springer Science and Business Media LLC
Date: 24-12-2022
DOI: 10.1038/S41598-022-26307-7
Abstract: A geometric digital twin (gDT) model capable of leveraging acquired 3D geometric data plays a vital role in digitizing the process of structural health monitoring. This study presents a framework for generating and updating digital twins of existing buildings by inferring semantic information from as-is point clouds (gDT’s data) acquired regularly from laser scanners (gDT’s connection). The information is stored in updatable Building Information Models (BIMs) as gDT’s virtual model, and dimensional outputs are extracted for structural health monitoring (gDT’s service) of different structural members and shapes (gDT’s physical part). First, geometric information, including position and section shape, is obtained from the acquired point cloud using domain-specific contextual knowledge and supervised classification. Then, structural members’ function and section family type is inferred from geometric information. Finally, a BIM is automatically generated or updated as the virtual model of an existing facility and incorporated within the gDT for structural health monitoring. Experiments on real-world construction data are performed to illustrate the efficiency and precision of the proposed model for creating as-is gDT of building structural members.
Autonomous production tracking for augmenting output in off-site construction
Publisher: Elsevier BV
Date: 05-2015
Using BIM for multi-trade prefabrication in construction
Publisher: Routledge
Date: 06-02-2020
Optimizing decisions in advanced manufacturing of prefabricated products: Theorizing supply chain configurations in off-site construction
Publisher: Elsevier BV
Date: 12-2017
Drones and Blockchain Integration to Manage Forest Fires in Remote Regions
Publisher: MDPI AG
Date: 30-10-2022
Abstract: Central management of fire stations and traditional optimization strategies are vulnerable to response time, a single point of failure, workload balancing, and cost problems. This is further intensified by the absence of modern communication systems and a comprehensive management framework for firefighting operations. These problems motivate the use of new technologies such as unmanned aerial vehicles (UAVs) with the capability to transport extinguishing materials and reach remote zones. Forest fire management in remote regions can also benefit from blockchain technology (BC) due to the facilitation of decentralization, t er-proofing, immutability, and mission recording in distributed ledgers. This study proposed an integrated drone-based blockchain framework in which the network users or nodes include drones, drone controllers, firefighters, and managers. In this distributed network, all nodes can have access to data therefore, the flow of data exchange is smooth and challenges on spatial distance are minimized. The research concluded with a discussion on constraints and opportunities in integrating blockchain with other new technologies to manage forest fires in remote regions.
Artificial Neural Network for Predicting Building Energy Performance: A Surrogate Energy Retrofits Decision Support Framework
Publisher: MDPI AG
Date: 14-06-2022
DOI: 10.3390/BUILDINGS12060829
Abstract: Assessing the energy performance of existing residential buildings (ERB) has been identified as key to improving building energy efficiency and reducing associated greenhouse gas emissions in Canada. However, identifying optimal retrofit packages requires a significant amount of knowledge of building energy modelling, and it is a time-consuming and laborious process. This paper proposed a data-driven framework that combines machine learning, multi-objective optimization, and multi-criteria decision-making techniques to evaluate the energy performance of ERB and thereby formulate optimal retrofit plans. First, an artificial neural network (ANN) was developed to predict the energy performance of a wide range of retrofit packages. A genetic algorithm was employed to determine the best structure and hyperparameters of the ANN model. Then, the energy consumption results were integrated with environmental and economic impact data to evaluate the environmental and economic performance of retrofit packages and thereby identify Pareto optimal solutions. Finally, a multi-criteria decision-making method was used to select the best retrofit packages among the optimal solutions. The proposed framework was validated using data on a typical residential building in British Columbia, Canada. The results indicated that this framework could effectively predict building energy performance and help decision-makers to make an optimal decision when choosing retrofit packages.
Collaboration in BIM-based construction networks: A bibliometric-qualitative literature review
Publisher: Elsevier BV
Date: 10-2017
Project Production Flows in Off-Site Prefabrication: BIM-Enabled Railway Infrastructure
Publisher: International Association for Automation and Robotics in Construction (IAARC)
Date: 22-07-2018
A qualitative investigation of perceived impacts of virtuality on effectiveness of hybrid construction project teams
Publisher: Emerald
Date: 02-01-2018
Abstract: The “virtuality” of a team collaborative interaction is the extent to which it is accomplished in the same place, in fully distributed virtual teams, or in a hybrid combination of the two. However, existence, strength and process of potential association between virtuality and effectiveness in construction project teams have remained elusive. This paper aims to address this gap in the literature. In this study, a conceptual model demonstrating the association between virtuality and effectiveness of teams was developed through integrating the input-process-output (IPO) model and the “Big Five” theory. This conceptual model was contextualised for the construction industry drawing upon conducting 17 semi-structured interviews with hybrid team experts. The findings provide the first model mapping the associations between virtuality and dimensions of team effectiveness for the construction context. The discovered patterns of associations between virtuality and dimensions of effectiveness for hybrid construction project teams (HCPTs) will assist managers in designing and running more effective teams. In addition, the findings help construction practitioners better understand how virtuality influence the performance and satisfaction of team members in HCPTs. The present study concludes with outlining a set of recommendations based on the findings of the study. As the first study in its kind, the present study offers a new insight into the concept and impacts of virtuality for construction teams and provides instructions and guidelines for designing and maintaining the effectiveness of such teams on construction projects.
Predicting the compressive strength of normal and High-Performance Concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer
Publisher: Elsevier BV
Date: 2020
Critical Literature Review of Labor Multiskilling in Construction
Publisher: American Society of Civil Engineers (ASCE)
Date: 2019
Concrete chloride diffusion modelling using marine creatures-based metaheuristic artificial intelligence
Publisher: Elsevier BV
Date: 11-2022
Role of Simulation in Construction Processes – Harmony in Capturing Resources
Publisher: Research Publishing Services
Date: 2012
Optimization modeling of multi-skilled resources in prefabrication: Theorizing cost analysis of process integration in off-site construction
Publisher: Elsevier BV
Date: 11-2018
A Bayesian smoothing for input‐state estimation of structural systems
Publisher: Wiley
Date: 06-07-2021
DOI: 10.1111/MICE.12733
Abstract: Instantaneous output‐only inversion of a system with delayed appearance of input influences on the measured outputs via filtering methods suffer from intensive lification of the observation noise in the estimated quantities due to the ill‐conditionedness. To remedy this issue, in this paper, a new unbiased recursive Bayesian smoothing method is developed for input‐state estimation of linear systems without direct feedthrough to reduce estimation uncertainty through an extended observation equation. By minimizing input and state estimation error variance, the optimal smoothing input and state gain matrices are derived. Moreover, a new efficient method is proposed for the recursive calculation of correlation of state estimation error with modeling and observation noise vectors.
Collaborative Role of Sociotechnical Components in BIM-Based Construction Networks in Two Hospitals
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2018
Assessing the Influence of Virtuality on the Effectiveness of Engineering Project Networks: “Big Five Theory” Perspective
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2018
A simulation model for investigation of operation of elevator’s up-peak
Publisher: Informa UK Limited
Date: 26-04-2019
Analysis of Citation Networks in Building Information Modeling Research
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2018
Safety in Off-Site Construction: Simulation of Crane-Lifting Operations Using VR and BIM
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2023
A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction
Publisher: Springer Science and Business Media LLC
Date: 22-11-2013
Barriers to the use of integrated project delivery (IPD): a quantified model for Malaysia
Publisher: Emerald
Date: 22-07-2019
DOI: 10.1108/ECAM-12-2018-0535
Abstract: The purpose of this paper is to quantify the barriers to the use of integrated project delivery (IPD), as assessed by 115 construction professionals in Malaysia. Barriers recording highest citation amongst researchers worldwide were collated in the form of a conceptual model. This model was validated via a partial least squares structural equation modelling technique. Findings advance the body of knowledge on IPD by providing original insights into the nature of key barriers, quantifying the relative importance of each barrier. Despite the above-mentioned contributions, and before drawing any conclusion, it is prudent to acknowledge limitations, particularly the chosen research approach in focusing on the Malaysian context. Therefore, caution must be exercised in direct application of findings to other contexts research findings should be seen through the lens of moderatum generalisations (see Payne and Williams, 2005). Apart from contributions to the body of knowledge, for the world of practice, conditions impacting a transition to IPD are discussed, with a recommendation for change management through a tested mechanism like the European Corporate Sustainability Framework. Being the first empirical study undertaken to quantify the relationship among the identified barriers and IPD, the present study contributes to the field by addressing the gap in IPD research in Malaysia, as an exemplar of a developing country it creates knowledge to inform further improvements in project performance through facilitating IPD use. The study also offers insight to construction stakeholders in other developing countries for tackling issues that hinder the adoption of an IPD approach, and it also points to major barriers such that resources for tackling barriers may be allocated properly.
Enhancement of bond characteristics between sand-coated GFRP bar and normal weight and light-weight concrete using an innovative anchor
Publisher: Elsevier BV
Date: 11-2023
Automatic compliance inspection and monitoring of building structural members using multi-temporal point clouds
Publisher: Elsevier BV
Date: 08-2023
Collaborative Scheduling of On-Site and Off-Site Operations in Prefabrication
Publisher: MDPI AG
Date: 08-11-2020
DOI: 10.3390/SU12219266
Abstract: As a kind of sustainable technology, prefabricated construction has increasingly gained momentum internationally due to its numerous benefits that include, but are not limited to, safe construction, waste minimization, quality improvement, and productivity enhancement. However, productivity in this domain is reliant on the efficiency of both on-site and off-site operations. On this basis, focusing on collaborative scheduling mechanisms, the current paper develops a static scheduling model and a dynamic scheduling model in prefabricated construction, and uses a simulated annealing algorithm (SA) to settle the optimization of operation planning considering delays by risks. The developed models are validated using data from a construction project with multiple suppliers of prefabricated elements. This study contributes to the body of knowledge in prefabricated construction management by streamlining collaborative scheduling in prefabrication. The established models provide construction managers with decision support systems with the aims of minimizing delays and related cost overruns.
Critical evaluation of off-site construction research: A Scientometric analysis
Publisher: Elsevier BV
Date: 03-2018
Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete
Publisher: Elsevier BV
Date: 11-2022
Optimal process integration architectures in off-site construction: Theorizing the use of multi-skilled resources
Publisher: Informa UK Limited
Date: 21-03-2017
Analysis of Workflow Variability and Its Impacts on Productivity and Performance in Construction of Multistory Buildings
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2015
The Less Agents, the More Schedule Reliability: Examination of Single-Point Responsibility Model in Design Management
Publisher: Springer Science and Business Media LLC
Date: 27-02-2019
Automatic far-field camera calibration for construction scene analysis
Publisher: Wiley
Date: 26-02-2021
DOI: 10.1111/MICE.12660
Abstract: The use of cameras for safety monitoring, progress tracking, and site security has grown significantly on construction and civil infrastructure sites over the past decade. Localization of construction resources is a crucial prerequisite for many applications in automated construction management. However, most existing vision‐based methods perform the analysis in the image plane, overlooking the effect of perspective and depth. The manual and labor‐intensive process of traditional calibration techniques, as well as the busy and restrictive construction environment, makes this a challenging task. This study proposes a framework for automatic camera calibration with no manual intervention. The framework utilizes convolutional neural networks for geometrical scene analysis and object detection, which are used to estimate the location of horizon line, vertical vanishing point, as well as objects with known height distributions. This enables automatic estimation of camera parameters and retrieval of scale. The proposed framework is evaluated on images from two major construction projects in Melbourne, Australia. Results show that the proposed method achieves a minimum accuracy of 90% in estimating proximity of points on the ground and can facilitate further development of vision‐based solutions for safety and productivity analysis.
Modeling carbonation depth of recycled aggregate concrete using novel automatic regression technique
Publisher: Elsevier BV
Date: 10-2022
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