Learning heuristics for determining slurry wall panel lengths

Determining panel lengths for slurry walls is an engineering issue that involves complex geotechnical, design, and site considerations. In practice, the decision is made through a trial-and-error process. Relevant principles extracted from experts are not sufficiently detailed to generate a solution. This research proposes an inductive learning model for solving this problem. Given a new project whose panel lengths need to be determined, the model chooses similar cases from existing cases, based on case-based reasoning, performs an inductive learning, and uses correctness and coverage rates, and then static rules to verify the induced results.