Concrete crack reasoning: Explainable defect diagnosis incorporating generative Pretrained transformer 4 and multimodal nondestructive testing data

초록

Timely detection of aging concrete deterioration requires diagnostic methods combining laboratory-level accuracy with field robustness. Existing models suffer from opaque decision-making and limited integration of multimodal sensor data. This paper presents Concrete Crack Reasoning (CCR), a field-validated diagnostic framework for concrete bridge defects that overcomes these limitations via a two-module pipeline: (i) sensorspecific convolutional neural networks distill features from ground penetrating radar, impact echo, and ultrasonic testing into concise, human-readable sentences; (ii) a memory-guided GPT-4 reasoning stage, operable in Baseline, Feedback, and Adaptive Refiner modes, the last of which uses prompt retrieval for self-correction. On a 1088-sample, four-class bridge-deck dataset, CCR raises top-1 accuracy from 49.4 % to 87.8 % and 97.9 %, reducing the 95 % bootstrap confidence interval to +/- 1.8 %. Explanations in Adaptive Refiner mode employ threshold-aware, cluster-referenced language consistent with expert practice, enhancing auditability. With lightweight in-model retrieval and no external databases, CCR is deployable on resource-constrained units, offering a practical path toward explainable AI-assisted structural health monitoring.

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