Reliability-Aware Selective Fusion of Visual and Class-Associated Acoustic Data for Concrete-Surface Classification Under Simulated Sensor Degradation

Automated concrete-surface classification increasingly employs multimodal sensing, although a wide range of systems assume that all sensors remain continuously available, reliable, and suitable for fusion. This study presents Reliability-Aware Selective Multimodal Fusion (RASMF), an image-first framework in which supplementary acoustic evidence is requested conditionally, assessed for signal-quality anomalies, fused when appropriate, and rejected when necessary. RASMF was evaluated on 4094 class-associated image-acoustic observations using 5 duplicate-aware outer folds and three random seeds under clean, simulated-degradation, and missing-modality conditions. At the nominal 10% acquisition budget, realised acoustic acquisition was 25.67%. Relative to the predefined missing-aware image-first baseline, mean error decreased from 1.9777% to 1.4499%, an absolute reduction of 0.5279 percentage points and a relative reduction of 26.69%, with a two-way bootstrap 95% confidence interval of −1.6220 to −0.0661 percentage points. The mean Brier score decreased from 0.03033 to 0.01688. RASMF produced lower mean error in 12 of 16 simulated-degradation and modality-loss conditions, with 6 condition-specific confidence intervals excluding zero in its favour. Increasing acoustic acquisition progressively reduced mean error and calibration error but increased processing demand; estimated pipeline time was 24.77 ms at the nominal 10% setting compared with 20.60 ms at the nominal 0% setting. A simpler logit stacker achieved a slightly lower mean error of 1.4295%, and the difference from RASMF was not statistically supported. The results therefore support RASMF as an explicit reliability-aware selective decision architecture relative to the designated baseline, without establishing universal predictive superiority over simpler fusion strategies.