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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 construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.

This paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.

This paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.

This study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?

Several collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.

This study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

The purpose of this study is to develop a decision-support framework that can be used by decision-makers to suspend public infrastructure projects. Additionally, the study also investigates how to select the most convenient infrastructure project for suspension.

The proposed framework includes an extensive set of factors and a novel comparison mechanism that can reveal the most convenient infrastructure project to be suspended. A comprehensible literature review and focus group discussion (FGD) sessions were conducted to identify factors that should be considered for suspension. Then, the neutrosophic analytic hierarchy process (N-AHP) method was used to determine the relative importance of the factors. Finally, the proposed comparison mechanism was demonstrated through a hypothetical case study and Technique for order of preference by similarity to ideal solution (TOPSIS) analysis.

Results showed that suspension decisions cannot be made merely based on “financial” factors. Instead, the other aspects, namely “Technical and managerial” and “Social and Environmental”, should also be taken into consideration. Second, factors related to the initial investment, cost of refinancing, cash flow, permits and approvals, insufficiency of bidders, degradation of the components, reputation, impact on stakeholders and criticality of the infrastructure were particularly elaborated as the most significant, needing the utmost attention of the decision-makers. Lastly, the results demonstrated that the proposed comparison mechanism has considerable potential to identify the most convenient infrastructure project for suspension.

Public infrastructure projects are often under pressure due to the inflationary state and economic stagnation of countries after major crises. The suspension decision for infrastructure projects necessitates comprehensible assessments to consider all consequences. Studies have widely investigated the contractual and legal aspects of project suspension in light of existing literature. However, little effort has been devoted to identifying the factors that decision-makers should consider before suspending a particular infrastructure project. Furthermore, existing literature does not investigate how to select the most convenient infrastructure project for suspension either. Thus, by developing a specific suspension framework for infrastructure projects by considering various factors, this study is the earliest attempt to examine the contract suspension mechanism of public infrastructure projects. In this respect, the study significantly contributes to the theory of contract management domain and has important managerial implications.

The risks and uncertainties of public–private partnership (PPP) projects threaten their sustainability. Contract flexibility, which is based on the theory of incomplete contract and transaction cost, may be a viable solution to this issue. The purpose of this study is to investigate the relationship between contract flexibility and the sustainability performance of PPP projects. The multiple mediating roles of justice perception and cooperation efficiency are assessed, thereby allowing the pathways and conditions to be understood more comprehensively for improving the sustainability performance of PPP projects.

Nine hypotheses in the proposed research model are tested via structural equation modeling using data acquired from 218 Chinese PPP professionals.

Results show that contract flexibility positively affects PPP project sustainability performance. Justice perception and cooperation efficiency play direct and sequential mediating roles in this effect.

This study validates that contract flexibility positively impacts the sustainability performance of PPP projects, where justice perception and cooperation efficiency serve direct and sequential mediating roles. The findings of this study contribute to an improved understanding of the effect of contract flexibility on the sustainability performance of PPP projects. Furthermore, they provide important theoretical and practical insights into contract management as well as beneficial information and valuable initiatives for improving the sustainability of PPP projects.

The smart contract provides an opportunity to improve existing contract management practices in the construction projects by replacing traditional contracts. However, translating the contracts into computer languages is considered a major challenge which has not been investigated. Thus, it is necessary to: (1) identify the obstructing clauses in real-world contracts; and (2) analyze the replacement's technical and economic feasibility. This paper aims to discuss the aforementioned objectives.

This study identified the flexibility clauses of traditional contracts and their corresponding functions through inductive content analysis with representative standard contracts as materials. Through a speculative analysis in accordance to design science paradigm and new institutional economics, the economic and technical feasibility of existing approaches, including enumeration method, fuzzy algorithm, rough sets theory, machine learning and artificial intelligence, to transform respective clauses (functions) into executable codes are analyzed.

The clauses of semantic flexibility and structural flexibility are identified from the contracts. The transformation of semantic flexibility is economically and/or technically infeasible with existing methods and materials. But with more data as materials and methods of rough sets or machine learning, the transformation can be feasible. The transformation of structural flexibility is technically possible however economically unacceptable.

Given smart contracts' inability to provide the required flexibility for construction projects, smart contracts will be more effective in less relational contracts. For construction contracts, the combination of smart contracts and traditional contracts is recommended. In the long run, with the sharing or trading of data in the industry level and the integration of machine learning or artificial intelligence reducing relevant costs, the automation of contract management can be achieved.

This study contributes to the understanding of the smart contract's limitations in industry scenarios and its role in construction project management.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

As the complexity and uncertainty of megaprojects make it difficult for traditional management models to address the difficulties, this paper aims to design a performance incentive contract through IT applications, thereby promoting the formation of an information-based governance mechanism for megaprojects and facilitating the transformation and upgrading of the construction management model of megaprojects to informatisation.

This paper introduced IT applications into the performance assessment and used the proportion of IT applications replacing traditional manual management as a variable. It analysed different replacement ratios to obtain the optimal solution for the change of contractors behaviours and promote the optimal performance incentive for the informatisation in megaprojects.

The results show that under the condition of the optimal replacement ratio, achieving the optimal state of a mutual win-win situation is possible for the benefit of both sides. The counter-intuitive finding is that the greater the replacement ratio is not, the better, but those other constraints are also taken into account.

This study enriched the research of the performance configuration incentive from a practical perspective. It extended the research framework of IT incentive mechanisms in the governance of megaprojects from a management theory perspective. It clarified the role of IT applications in incentive mechanisms and the design process of optimal incentive contracts under different performance incentive states. The incentives made the contractors work harder to meet the owner's requirements, and it could improve the efficiency of megaprojects, thus better achieving megaproject objectives.

The purpose of this study is to investigate the influences of the characteristics of procurement strategies, in this instance labour-only, on project performance concerning health and safety (H&S), a project performance indicator.

Using non-probability purposeful and snowballing sampling methods, questionnaires were used to collect data from construction professionals in Nigeria. This was then analysed using descriptive (frequency and mean scores) and inferential statistics (Mann–Whitney-U and Kendall's Tau_b tests).

The findings indicate a statistically significant negative correlation between ‘the level of client involvement and ‘fatalities' and a positive one with ‘conducting of health and safety risk assessment' and ‘conducting employee surveys on health and safety attitude’. Poor hygiene is found to be the worst lagging indicator, while conducting of inspection is the most adopted leading indicator of project health and safety performance. It also emerged that there is no significant difference in the health and safety performance of projects procured through the procurement strategy in urban and rural areas.

The study provides valuable insight into the complexities in H&S management due to the high level of client involvement in labour-only procurement system (LoPS) projects and the level of diversity in their responsibilities therein. It creates a fundamental direction for developing a detailed framework or guidance notes for client involvement in the integration of H&S into LoPS projects.

This is the first study that examines the influence of the characteristics of procurement strategy on project health and safety performance. Evidence in the literature shows that project delivery outcomes significantly improve if procurement is strategically used, including when it is considered early in projects. However, integrating H&S into procurement strategies has received little attention.

Cost overrun is one of the critical issues faced in construction projects, as nine out of ten projects will likely go over the budget. In particular, transportation infrastructure (TI) projects, such as roads and bridges, are vastly affected by cost overruns, which can delay the entire project. This research intends to identify the factors affecting the cost overruns in New Zealand (NZ) TI projects.

The research was conducted using a questionnaire survey involving ninety-two participants experienced in infrastructure project estimation in NZ. Quantitative methods were used to analyse the data.

Fifty-three factors were identified through literature under ten categories. Based on the survey, ten significant factors were identified with a high grade of importance. The three most critical factors were “frequent design changes,” “poor planning and scheduling” and “inadequate tender documentation.” It was found that the cost overrun is primarily affected by the pre-contract stage causes.

The data were collected from professionals involved in NZTI projects. Therefore, the implications may be different for other contexts.

The results will improve the current estimation practice by developing a new statistical model considering all the significant variables for NZTI projects.

Although much research is done to identify these factors, they are only considered in a few statistical cost models. These new statistical models mainly focused on technical variable factors similar to the current standard estimation process. However, the results of this research, qualitative and quantitative factors, will be used for the future cost model.

Cyber-physical systems (CPS) enable the synergistic integration of virtual models with the physical environment. This integration is gaining recognition for its potential to enhance construction project information management, thereby contributing to improved cost management in construction. Similarly, quantity surveyor (QS) plays a key role in construction projects by estimating and monitoring construction costs. Consequently, this research aims to explore redefining the role of QSs by integrating CPS.

The research adopted an interpretivism stance to collect and analyse data. Two rounds of 21 and 19 semi-structured interviews were conducted, with experts selected through heterogeneous purposive sampling. Code-based content analysis was used to analyse the data using NVivo12. MS Visio data visualisation tool was used to present the findings.

It is empirically proven the potential of CPS to facilitate nine key roles of QSs in all stages of the RIBA plan of work through the identification of 15 CPS applications and technologies. Nine key roles of QSs that CPS can facilitate were identified as preliminary estimation, measurement and quantification, contract administration, preparation of BOQ, interim valuation and payments, tender and contract documentation, cost planning, cost control and procurement advice. The study explored how adopting CPS technologies can transform traditional quantity surveying practices and enhance their value within the construction industry.

The findings add to the body of knowledge by redefining the role of QS through the integration of CPS for the first time and then by highlighting the usages of CPS in the construction industry rather than limiting it to a specific sector of the construction industry, as previous studies have done. This research uncovers several other research arenas on CPS as being the very first research to evaluate CPS to facilitate key roles of QSs. The findings can enhance the awareness and the practical implementation of CPS by intervening to form more partnerships among application developers and industry leaders.