The application of externally bonded carbon fiber-reinforced polymer (CFRP) stirrups is by now proven as an effective technique to strengthen slab-column connections for punching shear. However, there has not been adequate study into its effectiveness for repair and rehabilitation of existing slab-column connections with different levels of prior damage. The punching performance of predamaged slab-column connections rehabilitated with CFRP stirrups was investigated for the first time in this study. Two series of 2/3 scale slab-column connections with two different concrete compressive strengths (low and normal) were rehabilitated using CFRP stirrups at different damage levels. The specimens were all tested under a concentric punching load. Test results showed that externally applied CFRP stirrups could restore the punching shear capacity of fully damaged slabs to levels above their undamaged conditions. The lower concrete strength proved to have no detrimental effect on the effectiveness of the CF...

Pile-wharf structures are one of the substantial facilities for marine transportation along the shorelines. Due to their logistical significance, keeping the pile-wharf structures operational under all circumstances is crucial. Seismic forces are one of the major threats that can disrupt the functionality of the pile-wharf structures. This paper focuses on the seismic evaluation and upgrade of an existing wharf structure in Turkey by using the performance-based evaluation principles of a displacement-based design approach. According to the insufficient seismic performance of the existing wharf structure, two retrofitting schemes were proposed that considered the practicality and cost-efficiency aspects. In both schemes, additional piles were used to withstand the seismic demands, eliminate the torsional irregularity, and increase the lateral stiffness of the structure. The key differences between the proposed retrofitting schemes were the numbers and layout patterns of the additional piles. The numbers of additional piles used in the first and second schemes were 18 and 15, respectively. This study indicates that the performance-based evaluation of wharf structures using a displacement-based design approach provides a thorough understanding of the nonlinear behavior of marine structures and that the addition of piles is a reliable method for improving the seismic performance of pile-wharf structures.

Accurate prediction of earthquake—induced building damage is essential for timely disaster response and effective risk mitigation. This study explores a machine learning (ML)-based classification approach using data from the 2015 Gorkha, Nepal earthquake, with a specific focus on reinforced concrete (RC) structures. The original dataset from the 2015 Nepal earthquake contained 762,094 building entries across 127 variables describing structural, functional, and contextual characteristics. Three ensemble ML modelsGradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were trained and tested on both the full dataset and a filtered RC-only subset. Two target variables were considered: a three-class variable (damage_class) and the original five-level damage grade (damage_grade). To address class imbalance, oversampling and undersampling techniques were applied, and model performance was evaluated using accuracy and F1 scores. The results showed that LightGBM consistently outperformed the other models, especially when oversampling was applied. For the RC dataset, LightGBM achieved up to 98% accuracy for damage_class and 93% accuracy for damage_grade, along with high F1 scores ranging between 0.84 and 1.00 across all classes. Feature importance analysis revealed that structural characteristics such as building area, age, and height were the most influential predictors of damage. These findings highlight the value of building-type-specific modeling combined with class balancing techniques to improve the reliability and generalizability of ML-based earthquake damage prediction.

In performance-based earthquake engineering (PBEE), fragility curves hold significant importance for a reliable risk assessment of the existing reinforced concrete (RC) structures. Fragility curves require numerous incremental nonlinear dynamic analyses, which are highly time-consuming and computationally intensive. However, predicting the fragility curve parameters of RC structures by a machine learning algorithm could effectively reduce this cost. In this study, machine learning (ML)-based numerical analyses were performed in order to predict the fragility curve parameters of the existing RC structures, considering rapidly observable structural parameters by street survey. The construction date, story number, plan irregularities, soft story, and damage states are the main variables that are considered in this study. Hence, a dataset comprising the results of 620 structural fragility analyses was compiled from the existing literature. Key fragility parameters, namely the median and standard deviation, are predicted using several machine learning algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs). The performance of the proposed models is evaluated using R², RMSE, and MAE metrics under a five-fold cross-validation scheme. Furthermore, nonlinear dynamic analyses are conducted on a representative set of structural models to validate the machine learning predictions. The results indicate that the ANN model achieves the highest predictive accuracy, followed by ensemble tree-based methods, demonstrating the capability of machine learning approaches to effectively capture complex nonlinear relationships between seismic input parameters and structural response. The proposed framework significantly reduces computational effort while maintaining reliable prediction accuracy, offering an efficient tool for seismic risk assessment and fragility estimation of existing structures.

The use of plain (smooth and undeformed) rebars as longitudinal and transverse reinforcement in RC members is one of the most encountered practices in old construction technology. Thus, probable vulnerabilities due to plain rebar implementation should be considered in the seismic evaluation of RC buildings. It is known that reduced bond stresses distributed along the plain rebar surface due to the absence of the ribs leads to a resultant reduction in effective stiffness and an increase in rotation capacities for RC members. Therefore, RC buildings that were constructed according to the older design codes using plain rebars were expected to deform laterally beyond the anticipated deformation capacities that were determined based only on the RC members reinforced with deformed rebars. In this study, the modeling strategies were examined and verified with the results from an existing building test in the literature, and then the seismic performance of a five-story RC framed building, which was reinforced with plain rebars, was evaluated under nonlinear time-history analysis (NTHA). In order for code comparison, the nonlinear characteristics of the RC members were determined according to the previously suggested model for members reinforced with plain rebars and the current Turkish earthquake code regulations. The parameters investigated were (1) the number of members at each damage state, (2) the maximum interstory drift ratios, and (3) the normalized story shears by building weight considering the earthquake type (pulse type or non-pulse type) and predamage effects. The analysis results emphasized the importance of rebar characteristics in seismic performance assessment, because the use of plain rebars reduced the number of plastic hinges while leading to higher drift ratios. This phenomenon is analogous to the behavior of RC beams reinforced with plain rebars which fail with a smaller number of wide cracks rather than a greater number of narrow cracks as in the case of deformed rebars.