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Effective construction project planning relies on addressing constraints related to materials, labor, equipment, and others. Planning meetings are typical venues for stakeholders to identify, communicate, and remove constraints. However, a critical gap exists in lacking an automated approach to identify, classify, analyze, and track constraint discussions during onsite planning meetings. Therefore, this research aims to 1. develop a natural language processing model to classify constraints in meeting discussions; 2. uncover the discussion patterns of managers and foremen regarding various constraints; and 3. extract the root causes for constraints, evaluate their impacts, and prepare managers to develop practical solutions for constraint removal. This research collected meeting transcripts from 94 onsite planning meetings of a building project, spanning 263,836 words. Next, this research leveraged a general pretrained transformer (GPT) to segment discussion dialogs into topics. A Bidirectional Encoder Representations from Transformers (BERT)-based model was developed to categorize constraint types for each topic. The constraint patterns among meeting attendees were assessed. Furthermore, a GPT-based tool was devised to track root causes, impacts, and solutions for various constraints. Test results revealed an 8.8% improvement in constraint classification accuracy compared with the traditional classification model. An occupational characteristic in constraint discussion was observed in that the management team tended to balance their focus on various constraints, while foremen concentrated on more practical issues. This research contributes to the body of knowledge by leveraging language models to analyze construction planning meetings. The findings facilitate project managers in establishing constraint logs for diagnosing and prognosticating planning issues.

This research aims to determine the appropriate level of effort required for each step of the Observe–Plan–Do–Check–React (OPDCA) cycle to improve the workflow reliability of a construction project. Empirical data on detailed weekly meeting minutes over 18 weeks and the planned and actual starting and finish times of 475 activities were collected from a bridge construction case project. The information theory approach was utilized to measure the information gained from discussions pertaining to the OPDCA cycle during weekly planning meetings. Cooperative game theory and the Shapley notation of fairness were used to compute the contribution of each OPDCA step to workflow reliability. Results showed that “Observe”, “Plan”, “Do”, “Check”, and “reAct”, contributed 18%, 23%, 23%, 24%, and 12% to observed variations in workflow reliability measured by the percent plan complete (PPC). Also, findings revealed that synergy exists between the “Check” step and other steps in the OPDCA cycle. The methodology developed in this paper has potential implications for engineering managers. The method can be generalized to help project managers find the balance between planning and control efforts to improve workflow. It also provides proven techniques for continuous improvement during project execution to facilitate project success. Furthermore, at the organizational level, the developed method can help higher-level managers make informed investment decisions for employees’ training and development to improve performance in future projects.

To improve the service quality and convenience of bus travel services, this paper proposes the Intelligent Bus Travel Service Model (IBTSM). The IBTSM makes it possible to provide a travel strategy considering every aspect of bus travel, specifically, delay in the peak period arising from limited carrying-capacities of buses. A three-step approach was executed toward implementing the IBTSM. First, the bus travel-time was predicted using Long Short-Term Memory (LSTM). Next, the crowding level in the bus was evaluated using a fuzzy expert system, based on which a reasonable start-off time was planned, and the delay caused by large passenger flow was circumvented. The k-Nearest Neighbours (k-NN) algorithm was used to provide input data of passenger flow. In this study, the correlation between passenger flow variation and bus services was investigated to extend the provisions of the travel strategy to include start-off time scheduling and target bus selection, rather than only bus running-time estimation. The proposed model was evaluated using a bus in China as a case study, and its reliability and positive impact on promoting both the quality of bus services and development of intelligent travel were demonstrated.