By adopting a systematic literature and science mapping review, this paper aims to explore the impact of artificial intelligence (AI) on organizational justice and project performance. A total of 47 bibliographic records from the Scopus database were analyzed. The results revealed the annual publication trends of research articles and relevant peer-reviewed journals in the studied domain. It was found that while AI technology has made significant progress in several fields, its application areas in project management and organizational justice are still relatively low. Moreover, it objectively discussed the co-occurrence analysis of keywords, co-authors, countries/regions, and documents in the fields, revealing the current research topics. The main research topics include the (1) AI’s influence on organizational justice, decision analysis, and digital transformation, (2) fostering organizational justice and AI’s role in enhancing project performance, and (3) improving organizational performance approaches. Furthermore, this paper proposed research gaps and future research directions, including (1) advancing business intelligence strategies, (2) unlocking AI technology potential on organizational justice and project performance, (3) the adaption of cultural, diversity, environmental, and social factors, (4) the impact of AI on complex and challenging leadership styles, and (5) developing a comprehensive understanding of the agile framework. The findings of this paper could contribute to a better understanding of how AI shapes project/construction management and organizational justice, providing practical solutions for innovative development for researchers and policymakers.

Tender price evaluation of construction project is one of the most important works for the clients to control project cost in the bidding stage. However,the previously underutilization of project cost data made the tender price evaluation of new projects lack of effective evaluation criterion, which brings challenge to cost control. With the improvement of companies’ information technology application and the advent of big data era, the project cost-related data can be completely and systematically recorded in real time, as well as fully utilized to support decision-making for construction project cost management. In this paper, a system for tender price evaluation of construction project based on big data is presented, aiming to use related technique of big data to analysis project cost data to give a reasonable cost range, which contributes to obtaining the evaluation criterion to support the tender price controls. The paper introduced the data sources, data extraction, data storage and data analysis of the system respectively. A case study is conducted in a metro station project to evaluate the system. The results show that the system based on big data is significant for tender price evaluation in construction project.

Planning of construction technical specifications (CTS) for deep foundations is critical for ensuring works performed safely. $k$ -nearest neighbors (kNN) is regarded as a practical algorithm for case retrieval in a case-based reasoning (CBR) cycle to search for past similar plans for new plan making. The parameter $k$ and neighbors’ weights affect the performance of the CBR cycle deeply but kNN neglects the weights’ effect on case retrieval. The massive and multisource data of CTS of deep foundations presents a challenge for retaining case data in a database and for decision making due to an inefficient data process of the traditional tool. This paper presents a new framework to integrate weighted $k$ -nearest neighbors (kkNN) to improve the performance of a CBR system for technical planning of deep foundations. It contains two parts: (1) a process to deal with a large amount of data derived from CTS; and (2) kkNN to obtain similar cases considering $k$ and the weights of neighbors’. The feasibility of the proposed approach is validated through a case study and the evaluation result shows that the approach enhances the performance of the CBR cycle in creating construction technical specifications in deep foundation projects.

The roofs of heritage buildings are subject to long-term degradation, resulting in poor heat insulation, heat regulation, and water leakage prevention. Researchers have predominantly employed feature-based traditional machine learning methods or individual deep learning techniques for the detection of natural deterioration and human-made damage on the surfaces of heritage building roofs for preservation. Despite their success, balancing accuracy, efficiency, timeliness, and cost remains a challenge, hindering practical application. The paper proposes an integrated method that employs a convolutional autoencoder, thresholding techniques, and a residual network to automatically detect anomalies on heritage roof surfaces. Firstly, unmanned aerial vehicles (UAVs) were employed to collect the image data of the heritage building roofs. Subsequently, an artificial intelligence (AI)-based system was developed to detect, extract, and classify anomalies on heritage roof surfaces by integrating a convolutional autoencoder, threshold techniques, and residual networks (ResNets). A heritage building project was selected as a case study. The experiments demonstrate that the proposed approach improved the detection accuracy and efficiency when compared with a single detection method. The proposed method addresses certain limitations of existing approaches, especially the reliance on extensive data labeling. It is anticipated that this approach will provide a basis for the formulation of repair schemes and timely maintenance for preventive conservation, enhancing the actual benefits of heritage building restoration.

Analyzing and understanding the occurrence and evolution mechanisms of construction accidents are important for construction safety management. This study proposed a hybrid approach of integrating the energy transfer model (ETM) and system dynamics (SD) theory to delineate the entire evolution stage of the construction accident. Specifically, the Fengcheng Power Plant construction platform collapse accident (FPCA) was taken as a practical case study. First, the ETM is applied to demonstrate the evolving nature of the accident. Then, the network of the accident-causing factors is constructed using the SD theory to analyze the dynamic change characteristics. The results indicate that the accident was caused by risk factors with complex interactions at the management level. An energy constraint failure occurred when the transfer of dangerous energy transpired at the physical entity level, inducing the event. The proposed approach can provide a useful reference for safety risk estimation and management in future major construction projects.