Construction Management Domain Research Articles

Whole Life Critical Factors Influencing Construction Project Performance for Different Objectives: Evidence from Thailand

This study aimed to identify the critical factors (CFs) influencing project performance and analyze their relative importance across multidimensional criteria throughout the project life cycle. Based on a literature review, 179 individual factors were categorized into nine performance criteria. A questionnaire survey was conducted with client representatives, consultants, and contractors in Thailand. The statistical analysis of 93 subjects showed that the significance levels of the performance criteria and CFs changed depending on the considered phase in the project’s life cycle. The analysis identified the three criteria (client satisfaction, quality, time) that were most frequently used to measure the success of a project. Furthermore, the findings revealed the top 10 CFs for each performance category throughout the life cycle. Notably, these findings provide compelling evidence of the following most important CFs across the performance measures: competence of project participants, adequate experience of project participants, the availability of competent staff, positive personal attitudes among project participants, participation in environmental initiatives by management, competent supervisors, effective project planning and control, the need for collaboration, and the professionalism of the project team’s services. This research provides insights into the factors that influence project success and have managerial implications for project stakeholders, facilitating informed decision making throughout the life cycle. Through establishing novel perspectives, this study enhances the knowledge base in the construction management domain.

Towards a Data-Rich Era: A Bibliometric Analysis of Construction Management from 2000 to 2020

The rapid development of new technologies has made the acquisition and processing of big data much easier and more accessible to various domains including construction management. This trend has resulted in numerous new technical or management issues in the field, as well as increased research needs. Thus, it is very necessary to invest and assess the past, present, and possible future developments of construction management-related studies. This paper provides a comprehensive bibliometric analysis of the articles published in this field over the previous two decades. The seven most popular research themes were identified and discussed with the data adopted in the related studies, including modern technology, waste management, performance management, risk management, project management, knowledge management, and organization management. Typical research data, research approaches, and future research directions were discussed. Emerging topics such as smart technology, sustainability, resilience, and human factors are suggested to be further explored in the domain. The review conducted in this study can provide some insights into what has been done and what can be achieved in future research in the construction management domain towards a data-rich era.

A Systematic Literature Review of Non-Compliance with Low-Carbon Building Regulations

Low-carbon building regulations are acknowledged as critical instruments to facilitate the building industry’s decarbonization transition. However, recent studies have shown that non-compliance with low-carbon requirements is under-researched, leading to a significant divergence between policy intentions and actual performance. In light of this, the paper aims to provide a synthesis of existing research on non-compliance with low-carbon building regulations. It does this using a systematic literature review combined with bibliometric and text mining techniques. Through reviewing 26 scholarly works from the last decade, the paper demonstrates a peak production year around 2015, the year of the Paris Agreement, with the USA and Australia as key countries of concern. Subsequently, the study reveals three focused research areas: the development of building policy during the low-carbon transition; the role of building energy performance requirements in achieving low-carbon buildings; and building energy code compliance. Findings suggest widespread non-compliance with building energy codes and also indicate influencing factors and associated enhancement strategies. Finally, the paper identifies gaps in the investigation of new forms of building energy codes; an inconsistent conception of compliance; and a lack of understanding in building practitioners’ compliance behavior. The study contributes to knowledge by providing future research areas in this under-researched topic and by successfully applying both bibliometric and text mining analysis in the construction management domain. This is found to have advantages in terms of time efficiency and objectivity. It also offers practical implications for industry by minimizing the gap between policy intentions and real compliance performance.

Buildability in the construction industry: a systematic review

PurposeConstruction management researchers have acknowledged that the use of buildability could improve outcomes of project. Efficient use of resources required for the procurement of construction projects is important for the economy. This study aims to aggregate the current knowledge on buildability within the construction management domain into an understandable whole using the systematic review approach.Design/methodology/approachAn interpretivist epistemological approach was used as a lens for the systematic review of published research on buildability. The selected articles cover the time period between 1987 and 2020. The articles published in 2021 and 2022 were excluded to ensure that the scope of the current study is distinct and clear. In this research, qualitative content analysis was used to scrutinise the selected journal papers.FindingsBased on the analysis of literature, the trends and gaps in the current knowledge on the topic of interest were identified. It was found that stakeholder’s knowledge and commitment play a huge role in the extent of adoption of buildability as a practice in the construction sector. Also, the study confirms that the use of buildability is beneficial to the project and its stakeholders.Originality/valueThe study maps the current state of knowledge on buildability and provides information on the gaps that could be explored in the future by researchers.

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

Bibliometrics and visualisation of research on trust in construction projects

The present study used scientific mapping analysis to describe the overall state of the literature on trust in construction projects (TC) and to gain a deeper understanding of its intellectual structure and research areas of interest. It focuses on 349 articles from 27 journals in the construction-management domain from the past 20 years. The influential journals, scholars and articles in the domain of TC were analysed. A qualitative follow-up discussion targets two main objectives: intellectual structure and research fronts. The TC intellectual structure involves topics such as relationships, project performance, contracts and behaviour, approached by way of multiple disciplinary fields, including ‘transaction, management and accounting’, which are supported primarily by organisational theory, organisational trust theory, trust theory and incomplete contract theory. Multi-case analysis and structural equation models are used widely as research tools. New key TC research areas include varying relationship characteristics, trust evolution, interpersonal trust and new problems in project governance.

Integrated priority decision index for risk assessment in chaos: cost overruns in transport projects

Purpose Cost overrun is inherent to project chaos, which is one of the key drivers of project failure. The purpose of this paper is to explore the critical elements of complexity-risk interdependency for cost-chaos in the construction management domain by utilizing a multi-criteria decision model. Design/methodology/approach A total of 12 complexity and 60 risk attributes are initially identified from the literature and using expert’s judgements. For the development of a structured hierarchy of key complexity and risk drivers, a real-time Delphi process is adopted for recording and evaluating the responses from experts. Afterwards, a pair-wise comparison using analytical network processing is performed to measure complexity-risk interdependencies against cost alternatives. Findings The findings of the integrated priority decision index (IPDI) suggest that uncertainties related to contingency and escalation costs are the main sources of cost overrun in project drift, along with the key elements such as “the use of innovative technology,” “multiple contracts,” “low advance payment,” “change in design,” “unclear specifications” and “the lack of experience” appear to be more significant to chaos in complexity-risk interdependency network. Research limitations/implications This study did not address the uncertainty and vulnerability exit in the judgment process, therefore, this framework can be extended using fuzzy logic to better evaluate the significance of cost-chaos drivers. Practical implications These results may assist the management of cost overrun to avoid chaos in a project. The proposed model can be applied within project risk management practices to make better-informed technical decisions in the early phases of the project life cycle where uncertainty is high. Originality/value This research addresses the importance of cost overruns as a source of project chaos in dynamic systems where projects reach the edge of chaos and progress stops. A new IPDI index contributes toward evaluating the severity of complexity and risk and their interdependencies which create cost-chaos in infrastructure transport projects.

Scientometric analysis of research on “remotely piloted aircraft”

Purpose Remotely piloted aircraft (RPA) systems have emerged as an established tool within the construction industry. Concurrent with this trend has been the rise in research on RPA, establishing this as a new field of study within the construction management domain. What is needed now is an assessment of the current state of research in this emerging discipline – its strengths and weaknesses – by which future research on RPA in construction may be guided. The purpose of this paper is to address this need. Design/methodology/approach A total of 59 peer-reviewed journal articles covering RPAs within the construction domain were systematically reviewed using a mixed-methods approach, utilizing qualitative-scientometric analyses techniques. Findings The results reveal a field of study in its fledgling stage, with a limited number of experts operating somewhat in isolation, from a limited number of institutions. Key publication outlets are identified, with the main focus of research being in the technical areas of remote sensing, photogrammetry and image processing. Practical implications The study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of RPAs in the industry. Originality/value The study exposes what is missing from current research: a broader consideration of organizational adjustments needed to accommodate RPA usage, economic analyses and impediments to wider acceptance.

A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies

PurposeIn the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.Design/methodology/approachThis paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.FindingsThe findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.Research limitations/implicationsThis study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.Practical implicationsThese results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.Originality/valueThis study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

Partner selection and performance evaluation framework for a construction-related virtual enterprise: a multi-agent systems approach

ABSTRACTTo exploit market opportunities, disparate enterprises can pool their core competencies to form a temporary organisation. This inter-organisational collaboration of enterprises is commonly referred to as a virtual enterprise (VE). However, the VEs’ partner selection problems influence their overall performance. Due to the limited construction-specific studies on the evaluation and selection of partners, the current research sought to address this knowledge gap by investigating the partner selection problem. The partners comprising 10 construction companies carrying out project tasks for a large building in Nairobi, Kenya, were evaluated. Data were collected using a sequential mixed-methods approach consisting of a focus group interview, questionnaires and algorithms. The study proposed the use of the partner selection and performance evaluation technique. This technique combines fuzzy approximate reasoning with the conventional analytic hierarchy process algorithm, designed to deal with imprecise evaluators’ judgments. A multi-agent systems approach was chosen to simulate VEs. Results show that the chosen techniques are both efficient and effective. In particular, the reduced group fuzzy analytic hierarchy process reduces the number of pairwise comparisons required when comparing a large number of attributes. Application to the construction management domain, in particular, is germane to the research.

IFC-Based Product Catalog Formalization for Software Interoperability in the Construction Management Domain

AbstractInteroperability of the heterogeneous applications used in the domain of construction management can be best achieved by using generalized and standardized representations of the needed product data together with the data from product catalog, thereby enabling faster and better management and decision-making in terms of cost efficient building element selection. However, even though various solution approaches have been proposed in the last years, a general approach based on an acknowledged building information model standard is still missing. In this paper, a new product catalog structure complying with the data schema of the Industry Foundation Classes (IFC) standard is presented. It allows for coherent integration of product and cost information, helping to achieve the interoperability of the involved actors and tools in collaborative project environments. The essence of the developed interoperability approach is in the consistent definition of a new IFC View for product catalogues that pulls t...

Planning-Based Approach for Fusing Data from Multiple Sources for Construction Productivity Monitoring

AbstractProject management tasks, such as productivity monitoring and cost estimation, require data to be fused from multiple data sources. Data fusion approaches incorporated in the existing research studies within the construction management domain support a specific task or a decision (e.g., labor productivity monitoring or defect detection). Hence, most of the previously developed approaches do not necessarily support tasks other than the ones that they were intended for. This paper describes an automated planning approach as a general way to fuse data from multiple sources to support construction productivity monitoring tasks. A prototype system, which incorporates two planning algorithms, was developed to validate the generality of the approach on the basis of representative queries of construction engineers and managers identified in previous research studies.

Multi-Agent Framework for General-Purpose Situational Simulations in the Construction Management Domain

The need for contextually rich education environments in construction management suggests the development of a general-purpose situational simulation framework that can be used by independent developers to build effective training environments. Design and implementation of such a framework involves an understanding of the reasoning processes underlying the construction management domain. These reasoning processes can be isolated using a conceptual classification of problems in the construction management domain into constraint satisfaction and planning. Such a classification allows us to distribute the different reasoning processes to autonomous agents that comprise the foundations of a multi-agent framework for building general-purpose situational simulations. The Virtual Coach is an implementation of the proposed framework. Experimental results from preliminary studies have shown the efficacy of the Virtual Coach as an educational tool.

Acquisition, Representation, and Validation of Subjective Knowledge

Abstract: This paper concentrates on the three main stages of expert system development: knowledge acquisition, knowledge representation (which includes the treatment of uncertainties), and verification and validation. The findings of a survey of expert system applications in the construction management domain are reported. A model is presented that regulates the process of creating an expert system and that eliminates the shortcomings of existing techniques used with knowledge of subjective nature. This model is based on the Delphi method, a tool used for collecting data and information to solve nonanalytical problems. It is proposed that the use of the Delphi method in the knowledge acquisition, knowledge representation, and validation stages of expert system development will improve user confidence in the final product.

A formal approach to schema design for engineering databases

With the continued use of databases in engineering applications and the continued proliferation of engineering data, the importance of proper database design needs increasing attention. This paper introduces a new data modeling methodology called Nijssen's Information Analysis Methodology (NIAM). NIAM is a graphical modeling language used to design conceptual schemas that can be mapped onto any database model such as the relational and hierarchical data models. The paper describes a procedure, called the Optimal Normal Form algorithm, for mapping a NIAM conceptual schema onto a fifth normal form relational data model. An example from the construction management domain is used to describe the principles and concepts of the NIAM modeling methodology.

NIAM Conceptual Data‐Base Design in Construction Management

With the continued use of data bases in engineering applications and the continued proliferation of engineering data, the importance of proper data-base design needs increasing attention. This paper presents a new data, modeling methodology called NIAM. NIAM is a graphical modeling language used to design conceptual schemas that can be mapped onto any data-base model, e.g., the relational and hierarchical data models. The paper describes a procedure, called the optimal normal form algorithm, for mapping a NIAM conceptual schema onto a fifth normal form relational data model. It then provides a brief background on the relational data model and the normalization process. Examples from the construction management domain are used to describe the principles and concepts of the NIAM modeling methodology.