Probabilistic assessment of spatial variability in concrete strength of bridge piers using schmidt hammer testing and Monte Carlo simulation
Abstract
The assessment of concrete strength and degradation in bridge structures is essential for ensuring structural safety and durability. However, traditional deterministic methods often fail to capture the variability and uncertainty of material properties under real operating conditions. This study evaluated the condition of existing bridge piers using a probabilistic approach based on experimental data. Experimental data were obtained using the Schmidt hammer and statistically processed to determine the mean values and standard deviations of concrete strength across different zones. The Shapiro–Wilk test indicated deviations from normality (p < 0.05). Nevertheless, for engineering applications, a normal distribution was adopted as an approximation in the Monte Carlo simulation to estimate the probability distribution of concrete strength, characteristic strength (5th percentile), and failure probability based on 5,000 simulation iterations. The results revealed considerable spatial variability in concrete strength among the investigated sides. Monte Carlo simulation identified Side 3 as the most critical zone, exhibiting the lowest characteristic strength and the highest probability of failure. The proposed probabilistic framework can support condition assessment, maintenance planning, and risk-informed decision-making for the management of existing bridge piers.