This article proposes a method for assessing fatigue damage in reinforced concrete bridges based on a train-bridge coupled dynamic analysis system. The analysis system can be used to perform coupled vibration analyses of the bridge’s behavior during train passages. The Palmgren–Miner rule is used to estimate the linear accumulation of fatigue damage based on recommended S–N relationships for reinforced concrete from various codes. The method can be used to evaluate the dynamic performance of a bridge in conjunction with moving load (ML) and moving spring-mass-damper (MSMD) vehicle models. Calculated dynamic stresses are used to evaluate the fatigue damage at critical locations. A case study is presented focusing on the Långforsen Bridge, a railway arch bridge with a span of 89 m in northern Sweden. Fatigue assessments are conducted by combining the presented method with empirical measurements, and the effects of train speed and axle load on fatigue damage are investigated. Train-bridge coupled dynamic analysis using the proposed method provides a feasibly accurate solution for fatigue damage prediction in the four studied load cases. For low speeds and light loads, the two vehicle models yielded identical predictions of cumulative fatigue damage. However, their predictions diverged significantly for higher speeds and/or axle loads.

Using rice husk ash (RHA) as a cement substitute in concrete production has potential benefits, including cement consumption and mitigating environmental effects. The feasibility of RHA on concrete strength was investigated in this research by predicting the split tensile strength (SPT) and flexural strength (FS) of RHA concrete (RHAC). The study used machine learning (ML) methods such as ensemble stacking and gene expression programming (GEP). The stacking model was improved using base learner configurations ML models, such as, random forest (RF), support vector regression, and gradient boosting regression. The proposed models were validated by statistical tests and external validation criteria. Moreover, the effect of input parameters was investigated using Shapley adaptive exPlanations (SHAP) for RF and parametric analysis for GEP-based models. The analysis revealed that the stacking ensemble integrates base learner predictions and demonstrated superior performance, with R values greater than 0.98 and 0.96. Mean absolute error and root mean square error values for both SPT and FS were 0.23, 0.3, 0.5, and 0.7 MPA, respectively. The SHAP analysis demonstrated water, cement, superplasticizer, and age as influential parameters for the RHAC strength. Furthermore, the SPT and FS of RHAC can be predicted with an acceptable error using the GEP expressions in the standard design procedure.

In this study, tension-tension fatigue tests were conducted to investigate the residual stiffness degradation of carbon fiber-reinforced polymer (CFRP) tendons. Different stress levels were used in the tests, and measurements of residual stiffness and the number of loading cycles were taken. Based on experimental data for CFRP tendons, a quantitative residual stiffness model was developed by modifying Yao's model. This model is applicable to various stress levels. To assess its accuracy and applicability, the predicted results of this model were compared with those of cited models from other researchers. The findings revealed a three-stage degradation of residual stiffness in CFRP tendons under different stress levels. Furthermore, it was observed that the proportion of fatigue life accounted for by Stage III decreased with smaller stress ranges, while the proportion accounted for by Stage II increased. The proposed quantitative residual stiffness model was verified using both experimental and cited data.