A Fingerprint Matrix Methodology for the Predictive Diagnosis of Incipient Wall Failure: A Mathematical Modeling and Machine Learning Approach

Abstract

This study presents an integrated framework for structural health monitoring (SHM) of concrete infrastructures, combining multimodal sensor networks, finite element analysis (FEA), and deep learning (DL) to detect, localize, and prognose damage under coupled hydromechanical loads. A hybrid sensor array at 40% density achieved 92% coverage on a 7×5 m simulated panel, capturing raw fingerprints, strain (–0.046 to 1.869), moisture (–0.032 to 0.899), and acoustic (–0.161 to 3.595), with interpolation reducing noise by 28% and revealing temporal lags (τ=0.8 steps) and correlations (r = 0.13–0.36). FEA delineated damage hotspots (max 0.88), stress peaks (80 MPa), strain amplifications (0.0225), and moisture-displacement synergies (r = 0.84), compressing data via PCA (85% variance in PC1). ResNet-18 DL models on 5000 samples yielded failure classification accuracy 0.95–0.98 (AUC = 0.94, F1 = 0.589 binary), location prediction 0.80 (macro F1 = 0.81), with confusion matrices exposing hybrid biases (5% FP) and ROC/PR curves optimizing thresholds (AP = 0.59). Temporal confidences forecasted precursors (τ = 10 steps, r = 0.78), distributions matched actuals (KL = 0.15), and location under variability correlated with loads (r = 0.45, ANOVA F = 4.2). The pipeline bridges simulation-reality (RMSE = 0.07), enabling probabilistic alerts (P_f < 10^-3) and edge deployment (15 ms inference), potentially extending asset life 20% with 12:1 ROI amid climate stressors. This advances SHM from reactive to predictive, fostering resilient designs.