AI-Driven Sensor and Image Analysis Architecture for Construction Quality and Risk Monitoring

Construction quality and risk monitoring often relies on separated sources of evidence, including sensor readings, site photographs, inspection notes, and maintenance records. This separation makes it difficult to connect early warning signals with visual evidence and engineering context. This paper presents a modular AI-driven architecture for construction monitoring that integrates sensor observations, image embeddings, ontology-based knowledge representation, predictive analytics, and autonomous AI workflows.

The proposed architecture contains five main components. First, a sensor data layer collects environmental, operational, and equipment-related observations. Second, an image analysis layer uses image embeddings to compare site photographs, retrieve visually similar cases, and connect visual evidence with inspection records. Third, an ontology layer represents assets, locations, sensors, construction activities, defects, risks, and recommended follow-up actions. Fourth, a predictive analytics layer supports anomaly interpretation and early risk detection by combining sensor trends with visual and contextual evidence. Fifth, an AI-agent workflow layer coordinates evidence retrieval, validation, explanation, and generation of human-readable inspection summaries.

The main contribution of this work is the integration of these components into a single explainable decision-support framework for construction quality control, safety monitoring, equipment condition tracking, and predictive maintenance. The novelty lies in linking sensor anomalies with image-embedding-based visual similarity search and ontology-guided reasoning, supported by AI agents that organize the monitoring workflow. Compared with isolated dashboards or standalone machine-learning models, the proposed architecture provides more contextual, traceable, and actionable support for engineers and site managers.