This study investigated the mechanical and impact resistance properties of U-shaped polymer-modified concrete (PMC) incorporated with epoxy (EP) and polyacrylate (PA) binders. The polymer-modified concrete mixtures were prepared with varying binder contents (0 to 30%) at intervals of 10% for each EP and PA binder. Moreover, scanning electron microscopy (SEM) analysis coupled with energy-dispersive X-ray spectroscopy (EDS) was used to study the microstructure of the polymer-modified concrete mixtures. An Artificial Neural Network (ANN) model was developed to predict failure crack strength (N2). The results indicate that EP binders enhance impact resistance but decrease compressive strength, whereas PA binders slightly improve both mechanical and impact properties. Introducing the EP binder into the PCM mixtures reduces the compressive strength by 4.91%, 15.09%, and 33.02% for EP10, EP20, and EP30, respectively, compared to the reference specimen, whereas the impact strength at the initial crack strength was improved by 127.64%, 221.95%, and 17.07% for EP 10, EP 20, and EP 30, respectively. The ANN model demonstrated high accuracy in predicting N₂, achieving R² values of 0.9892 and 0.9664 during training and testing, respectively.

This study investigates the use of advanced convolutional neural networks (CNNs) to analyze and classify the fracture behavior of U-shaped concrete modified with polyurethane (PU) under repeated drop-weight impact loads. A total of 17 U-shaped specimens were tested under multiple drop-weight impact loads for each PU binder content (0%, 10%, 20%, and 30%) by weight of cement. By integrating digital image correlation (DIC) with dynamic and static mechanical testing, this research evaluates the concrete’s impact resistance and flexural behavior with varying PU binder content. Three CNN architectures, InceptionV3, MobileNet, and DenseNet121, were trained on a dataset comprising 1655 high-resolution crack images to classify the failure stages into no crack, initial crack, and advanced failure. Experimental results revealed that 20% PU content optimally enhances impact resistance and flexural strength, while mechanical properties declined significantly with 30% PU content. The strain localization in DIC analysis indicated reduced matrix cohesion, which was measured by the extent of strain concentration in the material, highlighting the importance of maintaining PU content below 20% to avoid compromising structural integrity. Among the models, InceptionV3 demonstrated superior accuracy (96.67%), precision, and recall, outperforming MobileNet (94.56%) and DenseNet121 (90.03%). The combination of DIC and deep learning offers a robust, automated framework for crack assessment, significantly improving accuracy and efficiency over traditional methods such as visual inspections, which are time-consuming and reliant on expert judgment.

This study examined the impact properties of ultra-high-performance concrete (UHPC) mixtures with steel fiber (SF) and retrofitted with polyurethane (PU) grouting using repeated drop-weight tests. Micro-steel fiber was added to UHPC mixes from 0 to 3% Vf, and PU grouting overlays of 5 mm, 10 mm, and 15 mm were applied. Digital image correlation (DIC) was used to analyze failure modes. The results showed significant impact durability and energy absorption improvements with increased SF content and thicker PU overlays. UHPC-15PU exhibited 363% and 449% higher first crack and failure strengths than UHPC-5PU. DIC analysis confirmed the failure patterns of the U-shaped UHPC specimen under impact load conditions.