Adopting sustainable building materials (SBMs) is one way to reduce the negative environmental impacts of the built environment. However, the adoption of SBMs is not as high as expected. This study aims to investigate which factors affect the adoption of SBMs.
A literature review and discussions with professional practitioners formed an initial list of variables affecting readiness to adopt SBMs. A questionnaire survey was designed, and data from industrial practitioners were collected using the snowball sampling method. The data collected from 218 respondents were analyzed using the PLS-SEM technique.
The findings show that SBM adoption is critically influenced by readiness at different levels (market, organization, and employee). Specifically, market readiness enhances organization preparedness (beta = 0.628), and organization readiness positively affects employee readiness (beta = 0.510). Market, organization, and employee readiness positively impact the adoption of SBMs (beta = 0.386, 0.364, and 0.158, respectively). Moreover, the relationship between market and employee readiness is fully mediated by organization readiness (indirect effect = 0.320; p = 0.000). Organization readiness positively mediates the relationship between market readiness and SBM adoption (indirect effect = 0.050; p = 0.001). The relationship between organization readiness and SBM adoption is fully mediated by employee readiness (indirect effect = 0.229; p = 0.000). Additionally, employee readiness positively mediates the relationship between market readiness and SBM adoption through organization readiness (indirect effect = 0.080; p = 0.000).
While past studies have predominantly focused on human perception, this study recognizes the importance of contextual factors like market conditions, Organization readiness, and employee readiness in shaping the adoption of SBMs. Thus, it fills a notable void in the literature and highlights the need for a broader theoretical framework that incorporates both individual perceptions and external contextual influences.
The primary objective of this study is to analyze the correlation between the application of building information modeling (BIM), the construction project success (CPS) and project control. Additionally, this research aims to explore the potential mediating effect of project control on the link between BIM and construction project success in the Malaysian construction industry.
The present study employs a quantitative research methodology and adheres to a positivist research philosophy in order to examine the influence of BIM implementation on the CPS and project control within the Malaysian construction sector. The data were collected by structured questionnaire survey from 200 respondents using a purposive sampling strategy, and the results were analyzed by using partial least square (PLS)-structural equation modeling (SEM). The study's findings are all the more noteworthy since, despite the small sample size, 218 persons genuinely participated thanks to the purposive sampling approach.
The findings of this study indicate that there are notable and positive associations between various variables related to the implementation of BIM. These variables include accurate quantities and estimations, risk management, facility management integration, optimized resource allocation and real-time monitoring. Furthermore, these relationships are observed to have a significant impact on both CPS and project control. The study revealed that project control serves as a mediator in the relationship between the deployment of BIM and the CPS in the Malaysian construction sector, with the exception of its influence on risk management.
The originality of this research consists in examining the mediating function of project control in the relationship between BIM implementation and CPS. This study contributes to the existing knowledge by enhancing our understanding of the processes via which BIM positively influences project outcomes.
This study addresses the global landscape of offsite construction, highlighting its variable adoption patterns and the challenge posed by the prevalent use of suboptimal decision-making methods. In response, the decision-making model seeks to equip decision-makers with tools for well-informed decisions on concrete construction systems, tailored to the unique characteristics of each project, in contrast to the persisting reliance on expert knowledge, checklists or similar tools.
The study extracts decision-making criteria through literature reviews, pilot studies and surveys amongst Australian construction professionals. A comprehensive comparison of four concrete systems against each identified criterion is conducted, followed by the application of an integrated decision model (Entropy-TOPSIS) to rank the systems, considering all criteria simultaneously. Real-world case studies validate the practical applicability of the model.
An analysis of 15 criteria demonstrated the multifaceted nature of selecting concrete construction systems, emphasising evolving industry priorities like time efficiency, environmental considerations and logistical constraints. The enduring appeal of in-situ concrete in complex projects underscores the significance of traditional methods. The integration of the Entropy-TOPSIS model proved to be a robust decision-making tool, enabling professionals to simultaneously consider all criteria and make well-informed, customised decisions.
The study’s originality lies in its comprehensive approach, considering diverse criteria and presenting a flexible decision-making model suitable for the dynamic demands of the construction industry.
Project management (PM) involves planning, allocating, directing and controlling project resources within a set of predetermined objectives. The modern definition of PM has evolved and grown into a broader concept. This paper supports the notion that PM evolved into four distinct phases: PM 1.0 is primarily concerned with planning, PM 2.0 with collaboration, PM 3.0 with proactive adaptation and PM 4.0 with using innovative technologies. Research efforts tackled critical aspects of PM, but none of them provided a clear foundation for the full context of PM principles and how they complement one another. This study fills this knowledge gap by investigating the evolution of PM over time.
The authors collected a dataset of research papers between 1960 and 2022 and performed a bibliometric analysis on the collected dataset to isolate the main trends that define the evolution of PM phases.
Results show that all PM phases overlap in terms of overarching themes, concepts, principles and contributions. More importantly, PM 5.0 may be around the corner to facilitate effective and efficient handling of time, cost, scope and risks within the ever-growing complexity of project initiatives.
This paper provides a data-driven study for a holistic understanding of the key trends in PM and the associated expectations of future research directions. This will be of interest to stakeholders within the overall PM domain and multidisciplinary work related to the construction industry.
The paucity of artisans in some construction trades and the transitioning of the experience of the few for sustainability calls for concern. Mentorship programmes offer a promising mechanism to support construction artisans through those transitions. Mentorship may enhance artisan decent work and economic growth, like increased income for artisans. This is part of Sustainable Development Goal 8 (Goal 8). Hence, this study aims to investigate issues hindering construction artisan skills growth and suggest measures to improve construction artisan skills through mentorship mechanisms to achieve Goal 8.
The study adopted a qualitative approach and collected data via oral interviews with knowledgeable participants. The participants were consultant experts in mentorship and Sustainable Development Goals (SDGs) matters, construction organisation management staffers and construction artisans in the Nigerian construction industry. The study covered Lagos and Abuja and achieved saturation at the 30th interviewee. Also, the research utilised a thematic method to analyse the collated data.
Enhanced knowledge sharing accelerates junior artisans’ learning skills quickly, improving artisans’ performance, ensuring sustainability of the skills learned (knowledge retention), encouraging collaboration, building the next generation of leaders and transitioning of experience to mentees emerged as mentorship’s role in developing construction artisans’ skills. Findings show that mentorship mechanisms to develop construction artisan skills face encumbrances. The perceived 32 encumbrances were re-clustered into mentee, mentor and government-related encumbrances. Also, achieving Goal 8 regarding construction artisans may be threatened if these issues are not checked. Thus, the study recommended measures improving construction artisan skills through mentorship mechanisms to achieve Goal 8.
Identifying the major encumbrances facing construction artisan skills improvement through mentorship would be useful to advocate measures to improve construction artisan’s skills to achieve Goal 8.
Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.
The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.
In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.
The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.
This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.
The construction of megaprojects often involves substantial risks. While insurance plays an important role as a traditional risk transfer means, owners and insurance companies may still suffer huge losses during the risk management process. Therefore, considering the strong motivation of insurance companies to participate in the on-site risk management of megaprojects, this study aims to propose a collaborative incentive mechanism involving insurance companies, to optimize the risk management effect and reduce the risk of accidents in megaprojects.
Based on principal-agent theory, the research develops the static and dynamic incentive models for risk management in megaprojects, involving both the owner and insurance company. The study examines the primary factors influencing incentive efficiency. The results are numerically simulated with a validation case. Finally, the impact of parameter changes on the stakeholders' benefits is analyzed.
The results indicate that the dynamic incentive model is available to the achievement of a flexible mechanism to ensure the benefits of contractors while protecting the benefits of the owner and insurance company. Adjusting the incentive coefficients for owners and insurance companies within a specified range promotes the growth of benefits for all parties involved. The management cost and economic benefit allocation coefficients have a positive effect on the adjustment range of the incentive coefficient, which helps implement a more flexible dynamic incentive mechanism to motivate contractors to carry out risk management to reduce risk losses.
This study makes up for the absence of important stakeholders in risk management. Different from traditional megaproject risk management, this model uses insurance companies as bridges to break the island effect of risk management among multiple megaprojects. This study contributes to the body of knowledge by designing appropriate dynamic incentive mechanisms in megaproject risk management through insurance company participation, and provides practical implications to both owner and insurance company on incentive contract making, thus achieving better risk governance of megaprojects.
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