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.

Reliable construction workflow relies on timely discovery, analysis, and checking of compliance with contract terms, which are time consuming and inefficient tasks. Smart contracts enabled by blockchain technology have demonstrated promise in addressing the inefficiencies of data communications due to their merits of traceability, immutability, transparency, and self-enforceability. However, a smart contract’s inability to interact with real-world data is the main issue that impedes further implementation. Today’s increasing availability of as-built data provides automatic condition assessments that have great potential to automate smart contract executions. This research area is uncharted territory for the industry. This research selects a case study to present an automatic decentralized management framework by exploring image-based deep learning solutions to automate and decentralize the conditioning of smart contract executions enabled by a web3.js-based decentralized blockchain application. It was found that the model can automate management intelligence with minimal workflow interruptions by timely identification of bottleneck activities and enforcement of mitigation strategies. Project managers can use the blockchain prototype to enhance information sharing, remove key risks, and enable a reliable workflow with minimal management efforts.

Collaborative scheduling (CS) and related practices impact the performance of construction projects which can be measured by key performance indicators (KPIs). However, little is known about which of these practices are implemented and how their combined implementation might impact KPIs. The objectives of this research are to identify perceptions of CS practices that drive project performance, define CS practices used by industry that impact KPIs, and establish practices that are more commonly implemented and have a higher potential to positively impact KPIs. The research team conducted a nationwide survey in the United States and obtained 241 usable responses. Utilizing an information theory approach to measure the uncertainty of implementation and impact of each practice, the research built a set of Chow–Liu tree models to determine the most efficient sequence of actions to improve CS. Results indicate that meeting owners’ expectation throughout the life-cycle of the project from design through construction and commissioning, using the schedule to support a strong project culture, and an effective communication plan were the top CS levers for overall KPI improvement. An innovative method was developed to help construction project managers discover the value of each CS practice, the relation between CS practices, and CS practices’ influence to project KPIs so that managers can improve KPIs efficiently by prioritizing their CS practices according to their own project needs.

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.

Workspace, project duration, and workforce are three critical resources for construction projects. Project managers need to expend time and effort reviewing, comprehending, and coordinating these resources. However, the space–time–workforce interactions and their impact on decision-making in project scheduling are not fully known. Therefore, the objectives of this research were to understand the impact of workforce shifts on space, time, and labor cost performances, develop a three-dimensional (3D) visualization tool to reveal activity-level resource dynamics, and associate the risk aftermath with the occurrence probability to balance subjective risk tolerance and objective system reliability. This research developed a simulation model based on a case project to compare the workflow of five major specialty trades (i.e., bar placer, carpenter, scaffolder, pipefitter, and concreter) in 267 scenarios. A resource-oriented 3D visualization tool was developed to help project managers monitor project schedules. The research established a risk control framework using value-at-risk (VaR) and conditional value-at-risk (CVaR) approaches to associate extreme outcomes with their occurrence probability. Simulation results indicated that pipefitters significantly affected workspace overlap, whereas bar placers and carpenters predominantly impacted project duration. The scholarly contributions are (1) the creation of an intelligent system to generate a project schedule from workforce assignments specified by project managers, (2) development of a tool to visualize the three-way resource dynamics of workforce, time, and space on a 3D model for all possible scenarios, and (3) development of a framework for project managers to balance planning strategies between subjective risk tolerance and objective system reliability. This research provides project managers with a dynamic 3D visualization of space, time, and workforce utilization and interaction in uncertain environments, further facilitating reliable project scheduling decision-making.