Current literature on relationship management has focused on formal mechanisms such as contracts but has neglected the inter-organizational behavior that determines the effective adoption of relational mechanisms in the context of megaprojects. This study examines the antecedents and consequences of relational behavior of participating organizations in megaprojects based on the theory of planned behavior (TPB). A sample of 285 senior managers working on China’s megaprojects was surveyed, and the results were analyzed using partial least square structural equation modeling (PLS-SEM), revealing that: (1) the adoption of relational behavior is equally motivated by an organization’s behavioral intention and perceived behavioral control (PBC), and (2) relational behavior, especially information exchange, contributes the most toward achieving a high project relationship quality. A closer examination of the data found that two moderating effects, namely project culture and prior experience with other participating organizations, were particularly important. These findings contributed to four implications for project participating organizations to cultivate relational behavior and improve relationship quality in megaprojects, including identifying potential behavioral benefits, enhancing relational competence, establishing collaborative culture, and reinforcing information sharing.

The exacerbation of safety risk levels in metro deep foundation pit construction is attributed to the interactive coupling of numerous risk factors. To comprehensively explore the underlying mechanisms of safety incidents, complex network theory is applied to analyze interactions among risk factors systemically. Initially, through the identification of safety risk factors, a risk factor system comprising six primary risk factors and 35 secondary risk factors is established. Subsequently, by utilizing coupling mechanism analysis and complex network theory, a coupling network model of safety risks in metro deep foundation pit construction with 42 nodes and 184 directed edges is constructed, with network topology indicators analysis revealing the evolutionary law of risk coupling. Finally, Python software is employed to simulate the network with single-node, random, and targeted immunization. Key risk factor nodes are identified using network efficiency measurement methods. The results indicate that all risk factors positively influence the connectivity of the coupling network, and the risk-coupling network presents small-world and scale-free characteristics. In comparison with different immunization strategies, targeted immunization is found to be more effective than random immunization, and prioritizing the control of risk factors with a high degree of centrality, such as “violation of operating regulations” and “inadequate safety supervision and hidden danger inspection”, proves more effective in alleviating risk coupling, underscoring the importance of prioritizing control of key risk nodes in the network. These findings provide a scientific basis for risk management and optimization in metro deep foundation pit construction.

The transfer-operate-transfer (TOT) project model is used widely as a commercial framework for public-private-partnerships to support provision of infrastructure and enable the delivery of services. However, operational delivery of such projects can encounter certain challenges, such as the need for improved revenue sharing between governmental and private partners. The purpose of this paper is to design a revenue sharing method (RSM) that satisfies the revenue-sharing forecast in the contract design stage and the realized revenue sharing in the contract execution period for an operational TOT project. This approach identifies the impact of external uncertainty and effort level as well as the input ratio on revenue sharing of participants, distributes and reasonably minimizes the project revenue uncertainty among the participants, and achieves an improved matching of the participants’ revenue sharing with their risk-sharing, resource input and effort level. The paper utilizes the fuzzy-payoffs Shapley value method for revenue distribution for an operational TOT project, where the fuzzy alliance and input ratio coefficient are adopted to gradually optimize the Shapley value and form the RSM of an operational TOT project. The RSM allows prediction of the revenue sharing of participations under uncertain conditions of project revenue and supports improved decision-making by participants.

Bidding for construction projects is a very important and representative field in the industry. The system of bidding for construction projects has made considerable progress over the years due to the accumulation of experience and many contributions to the field. Nowadays, with the rapid development of information technology and the intellectualization of the bidding system for construction projects, the accumulation and processing of data has become an essential element of its development. In order to manage the bidding system of construction engineering reasonably, this paper proposes a system based on image processing and pattern recognition technology, which can recognize patterns in the bidding process of construction engineering. Firstly, through binarization and other methods, the bidding project documents of construction projects are preprocessed in order to extract the main information from the documents; secondly, information is obtained through pattern recognition and processing; finally, the results of pattern recognition are imported into the bidding system, and the bidding information is quickly added to the database. This paper proposes a bidding system for construction projects based on image processing and pattern recognition. The system provides a quick and convenient means of importing the bidding information and allows personnel to efficiently process the bidding documents, particularly when there are numerous documents and and too much information. Finally, the user survey shows that the proposed bidding system based on image processing and pattern recognition can reduce the workload of construction project personnel, greatly facilitating the progress of the project.

With the development of China’s socialist market economy, public–private partnership (PPP) mode is providing a steady driving force for the new urbanization construction which has entered the fast lane. This mode not only revitalizes the abundant social capital, but also makes resource allocation more effective. As an important part of the new urbanization construction, the utility tunnel has been actively explored and piloted in the past 2 years. At the meantime, the risk management is the key to the success of the project. This paper is based on questionnaire survey to identify the risk factors of utility tunnel project in PPP mode, and uses SPSS 19 to test the reliability of the questionnaire. On this basis, the risk evaluation index system is designed, and then we use optimized fuzzy comprehensive evaluation to construct the risk evaluation model. Finally, an example is given to demonstrate the applicability of the risk evaluation model.

The safety risk analysis of urban elevated bridge construction is an important management method to reduce the loss of safety accidents, and it has significant scientific research value and engineering application value. Therefore, this study proposes a novel analysis method to address these challenges. Firstly, this paper constructs a Work Breakdown Structure (WBS)–Risk Breakdown Structure (RBS) matrix for the safety risk of urban elevated bridge construction in order to achieve a comprehensive and complete identification of the indicator system. Then, a combination of static weights and dynamic weights calculation methods is developed. The static weights are obtained using the analytic hierarchy process, while the dynamic weights are obtained based on the relationship between the dynamic scores of construction safety risk indicators in different construction stages and the preset evaluation levels. Finally, a case study of the Longlingshan elevated bridge project in Wuhan, China, is conducted to validate the feasibility of the proposed model and its potential application in projects. The case analysis for the first time reveals that with the progress of construction, the weights of each indicator continuously change, and the secondary indicators related to environmental factors, such as extreme high-temperature weather, undergo the greatest changes. A comparison of different dynamic weight calculation methods is conducted to highlight the advancement of the proposed model. The research findings of this paper will provide new insights and guidance for improving the construction safety of urban elevated bridge projects.