Knowledge-Based Digital Inspection System for Training Construction Inspectors

Abstract

State transportation agencies face growing challenges in training construction inspectors owing to a shrinking experienced workforce and the increasing complexity in inspection tasks. Traditional document-based training methods are often fragmented and lack contextual depth, limiting their effectiveness in preparing novice inspectors. This study presents a knowledge-based digital inspection training system that consolidates inspection information from multiple Indiana Department of Transportation (INDOT) documents—including Standard Specifications, General Instructions to Field Employees, Indiana Testing Methods, standard drawings, and Manual for Frequency of Sampling and Testing and Basis for Use of Materials—into a semantically structured knowledge graph. The inspection training system enhances learning through the integration of rationale, instructions, construction pitfalls, and failure scenarios associated with inspection tasks. A user-centered web application was developed to deliver this content in an intuitive format aligned with how inspectors naturally perform their duties—by pay item, construction process, or risk scenario. The system was evaluated through a mock exercise involving INDOT inspectors. Results showed that the system is effective in improving the construction inspectors’ confidence and understanding of inspection rationale, instructions, and risk consequences of missed inspection. This research contributes a scalable, risk-informed framework that improves accessibility and comprehension of inspection knowledge, with the potential to foster proactive inspection behaviors and support more consistent construction quality control across transportation projects.