As construction projects have significantly increased in size and become more complicated, the number of claims and dispute cases between participating parties during the construction work have been continuously increasing. To prevent such claims and disputes, the participants need to be assured of their contractual positions and rights based on contract facts. For this reason, the process of writing and reviewing the contracts for construction work is crucial. Most international construction projects require contract management teams to review all the possible risks in the contracts during the bidding periods. However, it is very difficult to review a vast number of contracts in a short period of time. Therefore, in this study, we proposed an automatic model of contract-risk extraction based on natural language processing (NLP) that can automatically detect the poisonous clauses of the contract in order to support contract management for construction companies (contractors). In validating the performance of the automatic model developed in this study, we found that the precision and recall were both 81.8% compared with manual review. This study is meaningful since a model has been developed that can carry out a preemptive contract-risk review.

As the world construction market becomes more globalized, an increasing number of construction companies, including both large companies (LCs) and small and medium-sized companies (SMCs), have extended their business to the global market. During this process, the number of collaborative ventures to mitigate the burden of entry risks has greatly increased. This study aims to investigate the formation of construction firms’ collaborative networks for performing international projects from both the LC and SMC network perspectives. To this end, 389 real cases of overseas projects executed by Korean firms over the last two decades that involved collaboration were analyzed using social network analysis (SNA) approach. The result showed that LCs and SMCs have different perspectives when pursuing collaborative ventures for overseas construction projects; LCs have a tendency to form large and dense networks, whereas SMCs prefer to maintain long-term and targeted relationships. Moreover, this study showed a series ...

As construction projects get larger and more complex, it has become clear that no single entity can have complete knowledge of or the abilities to handle all matters. Therefore, collaborative practices among heterogeneous individuals, which are temporarily congregated to perform a project, are required to accomplish common project objectives. On the basis of this premise, this paper introduces an agent-based simulation of the evolution of collaboration within interorganizational networks of construction project teams from game theory and social network perspectives. The simulation confirmed that the fewer individuals are familiar with others in the network, and thereby aware of networks they are involved in, the more time it takes for networks to reach stable states. It was also found that the tendency of cohesion increased as the effort to form relationships with outside partners rose. Furthermore, the results indicated that the more effort needed to form relationships with those from other organizations...

Defining, measuring, and dealing with contractual risks are crucial for successful construction projects because the contractual risks can lead to serious claims and disputes. In general, construction participants make a stipulation regarding their roles and responsibilities by contracting in order to prevent such claims and disputes. A common practice for preparing construction contracts is to modify the standard contract forms to reflect the interests of the given project from the owner’s perspective. In this process, however, favorable clauses that may be beneficial to the contractor are often modified or even removed, causing significant potential risks to the contractor. Therefore, an in-depth review of contract terms and conditions is required to avoid future risks. This study presents a new proactive risk assessment model to identify missing contractor-friendly clauses in the owner’s modified contract conditions from the contractor’s point of view. A case study is used to demonstrate the proposed framework, and real-world project cases were analyzed to understand what type of contractor-friendly clauses would likely be omitted in the owner’s modified contract. In this study, the developed model builds on rule-based natural-language processing (NLP) to analyze unstructured text data through preprocessing, syntactic analysis, and semantic analysis. The proposed data-driven risk assessment model is expected to reduce the extent of human errors by (1) identifying potential contractual risks that could arise disputes; and (2) supporting to develop an appropriate response strategy for the given risks.

Traditional construction planning, which depends on historical data and heuristic modification, prevents the integration of managerial details such as productivity dynamics. Specifically, the distance between planning and execution brings cost overruns and duration extensions. To minimize variations, this research presents a Building Information Modeling (BIM)-integrated simulation framework for predicting productivity dynamics at the construction planning phase. To develop this framework, we examined critical factors affecting productivity at the operational level, and then forecast the productivity dynamics. The resulting plan includes specific commands for retrieving the required information from BIM and executing operation simulations. It consists of the following steps: (1) preparing a BIM model to produce input data; (2) composing a construction simulation at the operational level; and (3) obtaining productivity dynamics from the BIM-integrated simulation. To validate our framework, we applied it to a structural steel model; this was due to the significance of steel erections. By integrating BIM with construction operation simulations, we were able to create reliable construction plans that adapted to project changes. Our results show that the developed framework facilitates the reliable prediction of productivity dynamics, and can contribute to improved schedule reliability, optimized resource allocation, cost savings associated with buffers, and reduced material waste.

Construction is one of the most dangerous occupations. The most prominent reason for worker accidents is unsafe behavior, particularly rooted in the inability to recognize and appropriately respond to risk factors. Previous studies have investigated worker perception and comprehension of construction hazards and safety information in laboratory settings. A relationship has been found between workload and workers’ situation awareness (SA), a useful concept for understanding risk perception and comprehension. However, it is necessary to investigate the actual construction environment to increase the real-world applicability of the research results. This experimental study analyzed workers’ SA in relation to physical and mental workloads acquired while performing work at a construction site. Perception (Level-1-SA) and comprehension (Level-2-SA) were measured using the SA Global Assessment Technique as subjects walked inside a facility under construction and performed inspection or construction works. Statistically significant differences were found between perception and comprehension of safety-related information. It was also found that mental load negatively affected SA. This study provided external validation of previously investigated effects of workload on workers’ perception and comprehension of safety-related elements at real construction sites. The study’s findings can improve the understanding of workers’ safety behaviors and help suggest directions for safety management guidelines. Graphical Abstract