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This study aims to demonstrate how the process of quality function deployment (QFD) is used to identify the basic requirements of the customers in designing and executing the commercial business center.

This study was considered with the aim of determining the approach of QFD methodology used in the planning and designing of commercial business centers. The methodology used in the study is a customer-driven process that includes customer requirements in each and every aspect of the planning and designing of the project. The main focus of this study is to understand the requirements of the customers and to design and execute a commercial business project.

This study illustrates the quality requirements of the projects that benefit from the QFD process to obtain customer requirements for the planning and designing of commercial business centers. A case study is used to demonstrate the use of QFD process. This helps to explain the effective application of QFD in the planning and designing of business centers and similar constructions.

The planning and designing of the commercial business center using the QFD process were challenging and hence it is limited to the design part. The strategic objectives are not taken into account while performing QFD in this case study and the risk of market research is lacking. House of quality (HOQ) can be too complicated at times; hence, the adaptability in the traditional QFD is lacking. Most of the work in the HOQ matrix is done through subjective evaluation. Therefore, this research is mostly useful for a single party responsible for all phases of the planning and designing of the project.

In the construction industry, the use of the QFD process for project performance analysis and application is restricted. As a result of the scarcity of studies on the planning and design of construction projects, this study on the planning and design of a construction project was inspired.

Circular construction offers sustainable solutions and opportunities to disentangle a project’s life cycle, including demolition, deconstruction and repurposing of architectural, civil engineering and infrastructure projects from the extraction of natural resources and their wasteful usage. However, it introduces additional layers of novel risks and uncertainties in the delivery of projects. The purpose of this study is to review the relevant literature to discover, classify and theorize the critical risk factors for circular construction projects.

The paper conducted a systematic literature review to investigate the risks of circular construction projects. It deployed a multistage approach, including literature search and assessment, metadata extraction, citation frequency analysis, Pareto analysis and total interpretive structural modeling.

Sixty-eight critical risk factors were identified and categorized into nine broad taxonomies: material risks, organizational risks, supply chain risks, technological risks, financial risks, design risks, health and safety risks, regulatory risks and stakeholder risks. Using the Pareto analysis, a conceptual map of 47 key critical risk factors was generated for circular construction projects. A hierarchical model was further developed to hypothesize the multiple possible connections and interdependencies of the taxonomies, leading to chain reactions and push effects of the key risks impacting circular construction projects.

This study constitutes the first systematic review of the literature, consolidating and theorizing the chain reactions of the critical risk factors for circular construction projects. Thus, it provides a better understanding of risks in circular construction projects.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Construction enterprises are achieving the goal of production safety by increasingly focusing on the critical factor of “human” and the impact of individual characteristics on safety performance. Emotional intelligence is categorized into three models: skill-based, trait-based and emotional learning systems. However, the mechanism of action and the internal relationship between emotional intelligence and safety performance must be further studied. This study intends to examine the internal mechanism of emotional intelligence on safety performance in construction projects, which would contribute to the safety management of construction enterprises.

A structural equation model exploring the relationship between emotional intelligence and safety performance is developed, with political skill introduced as an independent dimension, situational awareness presented as a mediator, and management safety commitment introduced as a moderator. Data were collected by a random questionnaire and analyzed by SPSS 24.0 and AMOS 26.0. The structural equation model tested the mediation hypothesis, and the PROCESS macro program tested the moderated mediation hypothesis.

The results showed that construction workers' emotional intelligence directly correlates with safety performance, and situational awareness plays a mediating role in the relationship between emotional intelligence and the safety performance of construction workers. Management safety commitment weakens the positive predictive relationships between emotional intelligence and situational awareness and between emotional intelligence and safety performance.

This research reveals a possible impact of emotional intelligence on safety performance. Adding political skills to the skill-based model of emotional intelligence received a test pass. Political skill measures the sincere and cooperative skills of construction workers. Using people as a critical element plays a role in the benign mechanism of “Emotional Intelligence – Situational Awareness – Safety Performance.” Improving emotional intelligence skills through training, enhancing situational awareness, understanding, anticipation and coordination and activating management environment factors can improve safety performance. Construction enterprises should evaluate and train workers' emotional intelligence to improve workers' situational awareness and safety performance.

This study aims to develop a constructability index (CI) that can ease the construction activities in a project based on the contractors’ experience and resources. The proposed CI is a vital decision support tool that quantifies the difficulty level for the contractor to execute certain activities with the contingency of other project elements. The virtual reality (VR) technology was used to provide additional data, communicate the contingency impact of other project elements on specific activities and provide sequential execution data to the contractors. This can minimize the risk of not being able to execute various activities on time and within the budget.

The VR-based CI was developed through two steps. Step 1 was to identify the factors affecting constructability by exploring the literature and consulting local construction experts. These factors were then organized through a hierarchy of main factors and subfactors and validated by local experts through predesigned surveys. The factors were classified into VR dependent or non-VR independent, and their relative weights were calculated using the analytical hierarchy process along with their reliability, which was determined using Cronbach’s alpha approach. Step 2 was to define the attributes for the constructability factors defined in Step 1 using the Multi Attribute Utility Theory to quantify the contractor’s compliance level of these factors by giving them the appropriate score. The utility factors for the VR-independent factors were obtained through standards, literature and local surveys, and they were quantified on a 1–10 scale. However, the VR-dependent factors were given their corresponding scores using the developed VR navigation environment generated by integrating Autodesk Revit and Navisworks software. Accordingly, the CI for each activity was evaluated, and the overall CI for the project was calculated by aggregating the CIs for all activities.

The developed CI quantifies the contractor’s ability to execute construction projects and addresses the lack of communication and coordination between the various construction units in the planning phase itself. Moreover, it can resolve possible hard (physical) and soft (time) construction clashes and minimize their impacts on project schedule and budget. Among the relative weights of the identified factors, prefabrication of building components was found to have the highest effect on constructability. Furthermore, applying the developed VR-CI, a real project showed that the utility values of the main factors quantified on a ten-point scale were between 6 and 9, which means routine supervisions and monitoring are required.

Though the concepts of constructability and VR have been used in different contexts, their integration to develop a comprehensive CI for the building construction industry is a unique contribution, which has not been reported previously.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.

With the increasing use of small unmanned aircrafts (SUAs), many countries have enacted laws and regulations to ensure the safe use of SUAs. However, there is a lack of industry-specific regulations accounting for the unique features of construction-related SUA operations. Operating SUAs in the construction industry is attributed to specific risks and challenges, which should be regulated to maximize the utility of SUAs in construction. This study, therefore, aims to develop a multi-dimensional regulatory framework for using SUAs in the construction industry.

A combination of quantitative and qualitative methods was used to compare seven selected national/regional SUA regulations to identify the applicability of implementing the existing regulations in construction. The interview surveys were then conducted to diagnose the challenges of construction-related SUA operations and gather interviewees' suggestions on the regulatory framework for SUA uses in construction.

The research found that some challenges of construction-related SUAs operations were not addressed in the current regulations. These challenges included the complex and time-consuming SUA operation permit, lack of regulation for special SUA operations in construction, insufficient regulatory compliance monitoring and a lack of construction-related remote pilots' training. A regulatory framework was then developed based on the findings of comparative analysis and interview surveys.

This study mainly compared seven representative countries/regions' regulations, leading to a small sample size. Further research should be carried out to study the SUA regulations in other places, such as South Africa, South America or Middle East countries. Besides, this study's respondents to the interviews were primarily concentrated in Hong Kong, which may cause the interview results to differ from the construction industry in other countries/regions. A large-scale interview survey should be conducted in other places in the future to validate the current findings.

The proposed regulatory framework provides a reference for the policy-makers to formulate appropriate industry-specific SUA regulations and improve the applicability of SUA regulations in the construction industry. It sheds light upon the future of SUA regulations and the development of regulatory practice in this area.

This study is the first to propose a multi-dimensional regulatory framework for operating SUAs in construction by comprehensive policy comparisons and interviews. The regulatory framework offers a fresh insight into the unexplored research area and points out the direction for subsequent studies on SUA regulations in the construction industry.

Information technology provides important support for on-site decision-making of field personnel. Most literature focuses on the technological aspects of decision-support systems, without fully understanding the information required for effective decision-making. This study aimed to formalize decision-makers’ requirements in terms of the major goals, decisions and information.

The situation awareness (SA) approach was applied through the goal-directed task analysis (GDTA) method, narrowing the scope to field managers’ decision-making during indoor construction activities. This method was based on a series of interviews to define, revise and validate the decision-making requirements for the given scope.

The study yielded 1,056 highly interrelated elements. The results indicate that the field manager’s overall goal is to execute and handoff work within the established deadlines, with the required quality, maximizing profits, within a safe work environment. The overall goal construes into five main goals regarding work progress, quality, costs, safety and communication. These goals include subgoals, decisions, and the information necessary to attain them, depicted in diagrams.

The findings allow enhancing the design of decision-support solutions by identifying information required for future developments and showing the interrelations between goals and information requirements that need to be addressed to present interfaces for effectively assisting on-site decision-making. Moreover, the results allow for the assessment of solutions regarding the sufficiency of information.

This is the first effort to fully understand the information required by field managers for on-site decision-making during indoor construction activities.