Dam engineering project success evaluation tool

Dam engineering projects are complex, long-term infrastructure undertakings that face persistent challenges in meeting time, cost, and performance expectations. Despite advancements in engineering practices, these projects often suffer from underperformance due to technical, environmental, and regulatory complexities. To address this issue, this study presents the development of the Dam Engineering Project Success Evaluation Tool, a decision-support application designed to evaluate and predict project success based on Critical Success Factors (CSFs) across the dam project lifecycle. The tool is underpinned by an Artificial Neural Network (ANN) model, which was trained on empirical data to capture non-linear relationships between CSFs and Success Criteria (SC). The tool provides a practical, user-friendly interface for project practitioners to assess project success during the planning, design, construction, and operation phases. Users input their level of agreement with statements reflecting key success factors, and the tool generates success predictions for each phase. The outputs are presented in percentage-based categories, allowing for straightforward interpretation of performance levels ranging from very low to high success. The ANN model integrates historical data and expert input to produce robust predictions, enabling users to identify areas that require attention and to make informed decisions for improving project outcomes. Validation interviews with experienced dam engineering professionals confirmed the tool’s ease of use, clarity of input and output structure, practical applicability, and overall predictive accuracy. Participants highlighted its potential to support proactive project management and iterative assessment throughout a project’s lifecycle. The tool not only serves as a diagnostic mechanism but also facilitates strategic planning by revealing which factors most significantly influence success. Its structured format and predictive capabilities enhance transparency in project decision-making. The integration of artificial intelligence with domain-specific knowledge marks a substantial advancement in dam project management practice. This paper details the tool’s design, interface, data processing logic, and validation process. It contributes to the body of knowledge by offering a replicable approach for integrating predictive modelling into infrastructure project evaluation. The Dam Engineering Project Success Evaluation Tool stands as a valuable asset for improving performance outcomes in an industry defined by complexity and risk.