Numerical analysis of the tensioning cables anchorage zone of a bridge superstructure is presented in this paper. It aims to identify why severe concrete cracking occurs during the tensioning process in the vicinity of anchor heads. In order to simulate the tensioning, among others, a so-called local numerical model of a section of the bridge superstructure was created in the Abaqus Finite Element Method (FEM) environment. The model contains all the important elements of the analyzed section of the concrete bridge superstructure, namely concrete, reinforcement and the anchoring system. FEM analyses are performed with the inclusion of both material and geometric nonlinearities. Concrete Damage Plasticity (CDP) constitutive relation from Abaqus is used to describe nonlinear concrete behaviour, which enables analysis of concrete damage and crack propagation. These numerical FEM results are then compared with actual crack patterns, which have been spotted and inventoried at the bridge construction site.

This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.

Due to the large amount of binder and low water-cement ratio, high-performance cement composites have high compressive strength and a dense hardened cement paste microstructure. External curing is insufficient, as it cannot reach the interior parts of the structure, which allows autogenous shrinkage to occur in the inside. Lack of prevention of autogenous shrinkage and high restraint causes structural microcracks around rigid components (aggregate, rebars). Consequently, this phenomenon leads to the propagation of internal microcracks to the surface and reduced concrete durability. One way to minimize autogenous shrinkage is internal curing. The use of soaked lightweight aggregate to minimize the risk of cracking is not always sufficient. Sorption and desorption kinetics of fine and coarse fly ash aggregate were tested and evaluated. The correlation between the development of linear autogenous shrinkage and the tensile stresses in the restrained ring test is assessed in this paper. A series of linear specimens, with cross-section and length custom designed to match the geometry of the concrete ring, were tested and analyzed. Determination of the maximum tensile stresses caused by the restrained autogenous shrinkage in the restrained ring test, together with the approximation of the tensile strength development of the cement composites were used to evaluate the cracking risk development versus time. The high-performance concretes and mortars produced with mineral aggregates and lightweight aggregates soaked with water were tested. The use of soaked granulated fly ash coarse lightweight aggregate in cementitious composites minimized both the autogenous shrinkage and cracking risk.