Engineering, Construction and Architectural Management最新文献

Purpose

This study aims to develop a production-oriented approach for optimal mass-customisation of floor panel layouts in cross-laminated timber (CLT) buildings. The study enables meeting building clients’ unique floor plan requirements at an optimal cost and simultaneously enhances manufacturers’ profit by minimising material and manufacturing process waste.

Design/methodology/approach

The present research uses a hybrid approach consisting of field data collection, mathematical modelling, development of a Genetic Algorithm (GA) and scenario analysis. Field data includes engineered timber production information, design data and building code requirements. The study adopts the Flexible Demand Assignment (FDA) technique to formulate a mathematical model for optimising the design of mass timber buildings and employs GA to identify optimal production solutions. Scenario analysis is performed to validate model outputs.

Findings

The proposed model successfully determines the load-bearing wall placement and building spans and specifications of floor panels that result in optimal production efficiency and the desired architectural layout. The results indicate that buildings made of a single category of thickness of panels but customised in various lengths to suit building layout are the most profitable scenario for CLT manufacturers and are a cost-effective option for clients.

Originality/value

The originality of the present study lies in its mathematical and model-driven approach towards implementing mass customisation in multi-storey buildings. The proposed model has been developed and validated based on a comprehensive set of real-world data and constraints.

Purpose

Project bundling is an innovative practice that groups or bundles several infrastructure projects into a single contract. While project bundling has various benefits, agencies are facing some challenges when bundling their projects, including properly assessing the feasibility (or infeasibility) of project delivery methods (PDMs) of interest. More specifically, project owners face the challenge of properly selecting between traditional and alternative PDMs for their bundled projects. Although some research efforts were devoted to providing guidelines in relation to different aspects related to project bundling, no previous study was conducted to help project owners performing PDMs-related feasibility analysis for bundled projects, which differ from normal, singly delivered projects. To fill this knowledge gap, this paper develops a decision-support tool that assists agencies in deciding whether they should select a traditional or alternative PDM (i.e. whether to go with the Design-Bid-Build (DBB) PDM or not) for their bundled projects.

Design/methodology/approach

An analytical methodology comprised of four main steps was followed in this paper. First, an expert survey was developed and distributed to industry experts to quantify the importance of 25 project bundling objectives. Second, principal component analysis was used to determine the weights for the different project bundling objectives. Third, a series of statistical tests was implemented to identify different feasibility tiers. Fourth, a user-friendly decision-support tool was developed, and its capabilities were demonstrated.

Findings

The results showed that six tiers exist to classify the feasibility (or infeasibility) of traditional PDMs (i.e. the DBB method) for bundled projects. The research outcomes have also reflected that the following five project bundling objectives contribute the most to making traditional PDMs (i.e. the DBB method) more feasible for bundled projects: (1) Having well-defined design features; (2) Requiring prior knowledge or experience with similar project size and scope; (3) Completing the overall project on schedule; (4) Keeping rate of expenditures within cash flow plan; and (5) Acquiring specific legislative, regulatory and jurisdictional requirements early on.

Originality/value

This research adds to the body of knowledge by equipping agencies and project owners with a decision-support system that helps them identify whether traditional or alternative PDMs are more appropriate for the specific objectives of their bundling program(s). By making the right PDM decision, project owners can enhance their bundling practices (especially in relation to the PDM proper selection) and ultimately the performance of their bundled projects.

Purpose

The evolution of digital construction within Industrial Revolution 5.0 emphasises the harmonious integration of humans and technology, value-driven technology implementation, adaptive leadership and user-centric design solutions. This shift necessitates architects to enhance their soft skills to navigate the transition effectively. Failure to demonstrate these skills may confine architects to passive roles as technology consumers rather than innovative leaders. While existing research predominantly focuses on the technical aspects of digital construction, this study investigates how architects effectively manifest their soft skills within such dynamic contexts, addressing a critical gap in the current literature.

Design/methodology/approach

Employing an interpretive phenomenological approach, the research engaged 14 individual architects practicing in architectural consultancy practices (ACPs) in Malaysia through semi-structured interviews and written exercises to unveil their experiences.

Findings

Thematic analysis revealed six primary themes: “analytical and critical thinking,” “digital communication,” “diversity in leadership styles,” “emotional intelligence,” “ethics and professionalism,” and “curiosity and agility.”

Practical implications

Furthermore, the developed thematic map serves as a foundational resource for human resource practices aiming to design upskilling and reskilling programs that focus on nurturing soft skills. Emphasising soft skills, these programs are designed to thrive in the digital construction era and beyond.

Originality/value

These shared experiences significantly contribute to the theoretical understanding of soft skills within the digital construction context, providing novel insights into their practical application when addressing real-world implications.

Purpose

Computer vision (CV) offers a promising approach to transforming the conventional in-person inspection practices prevalent within the construction industry. However, the reliance on centralized systems in current CV-based inspections introduces a vulnerability to potential data manipulation. Unreliable inspection records make it challenging for safety managers to make timely decisions to ensure safety compliance. To address this issue, this paper proposes a blockchain (BC) and CV-based framework to enhance safety inspections at construction sites.

Design/methodology/approach

This study adopted a BC-enhanced CV approach. By leveraging CV and BC, safety conditions are automatically identified from site images and can be reliably recorded as safety inspection data through the BC network. Additionally, by using this data, smart contracts coordinate inspection tasks, assign responsibilities and verify safety performance, managing the entire safety inspection process remotely.

Findings

A case study confirms the framework’s applicability and efficacy in facilitating remote and reliable safety inspections. The proposed framework is envisaged to greatly improve current safety inspection practices and, in doing so, contribute to reduced accidents and injuries in the construction industry.

Originality/value

This study provides novel and practical guidance for integrating CV and BC in construction safety inspection. It fulfills an identified need to study how to leverage CV-based inspection results for remotely managing the safety inspection process using BC. This work not only takes a significant step towards data-driven decision-making in the safety inspection process, but also paves the way for future studies aiming to develop tamper-proof data management systems for industrial inspections and audits.

Purpose

Although the literature recognizes that coopetition plays a significant role in the success of international construction joint ventures (ICJVs), the impacts of coopetition on the performance outcomes of ICJVs remain largely unknown. This study extends this line of research by theorizing coopetition from three dimensions, i.e. coopetition intensity, coopetition balance and coopetition structure, and examining the relationships between coopetition and ICJV performance outcomes from both the contingency and configuration perspectives.

Design/methodology/approach

The hypotheses were tested using survey data from a sample of 188 ICJVs. Structural equation modelling was employed for the contingency approach to estimate the relationships between the three dimensions of coopetition and performance. For the configuration approach, cluster analysis was utilized to identify coopetition patterns. Subsequently, an analysis of variance was employed to analyse the relationships between these coopetition patterns and performance.

Findings

The contingency results indicate that while coopetition intensity is positively related to all types of performance, coopetition balance is only positively related to project performance and partner performance. Moreover, coopetition structure is only related to partner performance and socioenvironmental performance. The configuration approach identifies six patterns of coopetition, manifesting different levels of project, partner and socioenvironmental performance.

Originality/value

These findings, therefore, contribute to the ICJV literature by extending the understanding of how coopetition dimensions individually and jointly influence ICJV performance.

Purpose

There are many safety hazards in construction workplaces, and inattention to the hazards is the main reason why construction workers failed to identify the hazards. Reasonably allocating attention during hazard identification is critical for construction workers’ safety. However, adverse working environments in job sites may undermine workers’ attention. Previous studies failed to investigate the impacts of environmental factors on attention allocation, which hinders taking appropriate measures to eliminate safety incidents when encountering adverse working environments. This study aims to examine the effects of workplace noise and heat exposure on workers’ attention allocation during construction hazard identification to fill the research gap.

Design/methodology/approach

This study applied an experimental study where a within-subject experiment was designed. Fifteen construction workers were invited to perform hazard identification tasks in panoramic virtual reality. They were exposed to three noise levels (60, 85 and 100 dBA) in four thermal conditions (26°C, 50% RH; 33°C, 50% RH; 30°C, 70% RH; 33°C, 70% RH). Their eye movements were recorded to indicate their attention allocation under each condition.

Findings

The results show that noise exposure reduced workers’ attention to hazardous areas and the impacts increased with the noise level. Heat exposure also reduced the attention, but it did not increase with the heat stress but with subjects’ thermal discomfort. The attention was impacted more by noise than heat exposure. Noise exposure in the hot climate should be more noteworthy because lower levels of noise would lead to significant changes. These visual characteristics led to poorer identification accuracy.

Originality/value

This study could extend the understanding of the relationship between adverse environmental factors and construction safety. Understanding the intrinsic reasons for workers' failed identification may also provide insights for the industry to enhance construction safety under adverse environments.

Purpose

With the advancement of technology and more attention to environmental issues, building information modeling (BIM) and green building have become two new and growing trends in the construction industry. Therefore, this study proposes a new strategy that integrates BIM and green building rating assessments with an emphasis on Iran Green Building Rating System (IGBRS).

Design/methodology/approach

By creating a Revit-IGBRS project template that includes sheets related to all credits, the project compliance with the IGBRS credits and management of submittal documents for certification has been facilitated. Finally, a case study of the materials and resources category of the IGBRS system was performed to validate the BIM-IGBRS application model. All 8 criteria of this category were examined by using Dynamo programming for the Revit sample project.

Findings

A practical model for BIM and IGBRS integration is presented, which allows designers to be aware of the IGBRS scores obtained before the project’s construction phase and examine different scenarios for the highest scores. Overall, this study showed that integrating BIM and the Iranian rating system is possible with some constraints, and adding some features to BIM software can promote this integration.

Originality/value

Given that no study has been conducted on the integration of BIM with the Iran Green Building Rating System (IGBRS), the present research investigates utilizing building information modeling to meet the credits requirements of this rating system. The results of this research can be generalized and used in other green rating systems.

Purpose

Architectural programming, as a critical phase in construction projects, has been widely recognized for its importance and advantages throughout the construction process. With the rapid development of the socioeconomic landscape, architectural programming has garnered increasing attention from various other disciplines, becoming a key trend in interdisciplinary collaboration. This study aims to provide a comprehensive understanding of the current status and future directions of architectural programming from an interdisciplinary perspective through scientometric analysis and systematic review.

Design/methodology/approach

This study first collected English journal articles on architectural programming published between 1975 and 2024 from the Web of Science and Scopus databases. After an initial screening of titles and abstracts, 515 articles were selected for scientometric analysis to reveal the current state and advantages of architectural programming research in multidisciplinary fields. Subsequently, a second screening of full-text articles identified 75 journal articles for systematic review, focusing on research topics and challenges in interdisciplinary applications.

Findings

The study reveals an exponential increase in the number of papers related to architectural programming between 1975 and 2024, particularly in the last decade. Six key research topics of architectural programming in multidisciplinary fields were identified: (1) performance optimization and evaluation, (2) digitalization and automation development, (3) project management and decision support, (4) improvement of human and social welfare, (5) sustainable resources and environment and (6) educational practices of architectural programming. Additionally, the study identified the main challenges in the interdisciplinary application of architectural programming, including (1) incompatibility among disciplines, (2) limitations of data and methodologies and (3) insufficient social engagement. To address these challenges, three potential future directions were proposed: (1) establishing interdisciplinary teams and platforms, (2) enhancing multi-source data integration and digital transformation and (3) improving governance mechanisms and educational training.

Originality/value

By combining quantitative and qualitative methods, this study provides a comprehensive review of architectural programming research and applications in multidisciplinary fields, offering theoretical foundations and practical references for the future development of architectural programming. This review not only aids in understanding the overall status of current architectural programming research but also offers valuable insights and recommendations for future research directions and practical applications.

Purpose

This study aims to investigate the influence of external factors identified by the Political, Economic, Social, Technological, Environmental and Legal (PESTEL) framework on corporate social responsibility (CSR) performance in Vietnamese construction firms.

Design/methodology/approach

The snowball sampling method was employed to gather 182 validated responses. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research analyzed how these factors correlate with CSR practices under institutional theory.

Findings

Results indicated that social, economic, environmental, legal and technological factors positively impacted CSR performance. Among these, social factors had the most significant effect, followed sequentially by economic, environmental, legal and technological influences. Intriguingly, political factors demonstrated no significant association with CSR performance.

Research limitations/implications

The strong impact of social factors confirms that societal norms and cultural values are critical in shaping corporate behavior in Vietnam. Firms can leverage this insight by intensifying their community engagement and social investment. Additionally, the negligible role of political factors in shaping CSR suggests that firms might not need to focus heavily on political engagement in Vietnam. However, firms should remain aware of legal changes as legal factors influence CSR outcomes.

Originality/value

Despite CSR’s growing importance, there remains a notable research gap regarding how external macro-environmental factors influence CSR performance, particularly within the construction industry. The findings emphasize the importance of aligning business strategies with socioeconomic and environmental aspects.

Purpose

This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

Design/methodology/approach

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

Findings

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

Originality/value

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.