BridgeShield 2030: A Physics-Informed Multi-Sensor Evidence-Fusion Framework for Structural Health Monitoring of Highway Bridges in Hot-Arid Environments

Bridge structural-health-monitoring (SHM) systems can generate vibration, strain, displacement, temperature, surface-condition and data-quality signals, yet isolated alarms are vulnerable to environmental variability, sensor faults and incomplete evidence. This paper proposes BridgeShield 2030, a physics-informed, multi-sensor evidence-fusion framework for risk-based monitoring of highway bridges in hot-arid environments. Six transparent indicators are scored on ordinal 0-5 scales and fused into four action tiers, while hard-stop rules preserve the authority of qualified inspection and engineering assessment. The artifact was computationally evaluated using 320 reproducible synthetic monitoring windows spanning intact, minor, moderate and severe condition states across 10-55 degrees C. No employer, client, bridge-owner or live-project data were used. Temperature-compensated fusion achieved 90.3% four-tier accuracy and a macro-F1 of 0.902; raw, uncompensated fusion achieved 82.2%. Frequency-only and strain-only classifiers achieved 81.2% and 80.0%, respectively. Compensation reduced false alerts in intact windows from 8.8% to 1.2%. Mean tier stability was 97.9% under 1,000 lognormal weight perturbations per observation. Treating missing frequency and strain readings as satisfactory understated risk by 22.0 points across 63 affected windows. These results describe the controlled generator, not field performance. The contribution is an auditable decision-support architecture linking sensor evidence to inspection, finite-element interpretation, nondestructive evaluation and asset-management action. Field calibration, independent inspection, load-rating review and authorized decision-making remain mandatory.