Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
To explore how the water–cement ratio affects the mechanical behavior of concrete subjected to freeze–thaw cycles, four sets of concrete samples with water–cement ratios of 0.41, 0.44, 0.47, and 0.50 were prepared for laboratory analysis. These samples underwent varying numbers of freeze–thaw cycles (0, 10, 20, and 30) before being tested using the split Hopkinson pressure bar (SHPB) system for dynamic compression. The experimental data show that the mass of the concrete specimens follows a non-monotonic trend during freeze–thaw cycling, initially rising and then gradually declining. Simultaneously, key dynamic mechanical properties, such as compressive strength and elastic modulus, markedly deteriorate, as evidenced by rightward shifts in the stress–strain curves. Importantly, the extent of degradation differs notably depending on the water–cement ratio. Additional analysis highlights a strong association between the fractal nature of the fracture patterns and the effects of freeze–thaw cycles: under consistent freeze–thaw conditions not only does the fractal dimension consistently increase with the number of cycles, but it also positively correlates with the water–cement ratio....
Reinforced concrete structures often require retrofitting due to damage caused by natural disasters such as earthquakes, floods, or hurricanes; deterioration from aging; or exposure to harsh environmental conditions. Retrofitting strategies may involve adding new structural elements like shear walls, dampers, or base isolators, as well as strengthening the existing components using methods such as reinforced concrete, steel, or fiber-reinforced polymer jacketing. Selecting the most appropriate retrofit method can be complex and is influenced by various factors, including initial cost, long-term maintenance, environmental impact, and overall sustainability. This study proposes utilizing an artificial neural network (ANN) to predict sustainable and cost-effective seismic retrofit solutions. By training the ANN with a comprehensive dataset that includes jacket thickness, material specifications, reinforcement details, and key sustainability indicators (economic and environmental factors), the model was able to recommend optimized retrofit designs. These designs include ideal values for jacket thickness, concrete strength, and the configuration of reinforcement bars, aiming to minimize both costs and environmental footprint. A major focus of this research was identifying the optimal number of neurons in the hidden layers of the ANN. While the number of input and output neurons is defined by the dataset, determining the right configuration for hidden layers is critical for performance. The study found that networks with one or two hidden layers provided more reliable and efficient results compared to more complex architectures, achieving a total regression value of 0.911. These findings demonstrate that a well-tuned ANN can serve as a powerful tool for designing sustainable seismic retrofit strategies, helping engineers make smarter decisions more quickly and efficiently....
Probabilistic analysis of overtopping is an important aspect of dam construction, and the uncertainty in the construction progress complicates the calculation of overtopping probabilities. Construction progress is significantly influenced by human factors, making it difficult to assign an accurate probability distribution. Although the interval non-probabilistic reliability (INPR) method can estimate the likelihood of events with unknown probability distributions, its calculation results for the overtopping probability have significant errors. To improve the rationality of the results, we developed a method that adopted flood frequency of the upstream flood level to correct the traditional INPR calculations, developing an improved model. Taking the Qianping Reservoir as an example, the overtopping probabilities for three construction schedules were calculated, and the results were compared with a flood routing calculation. The results indicated that the improved approach significantly improves the rationality of the calculations, and the results effectively reflected the impact of construction progress uncertainty on overtopping probabilities....
The aim of this study is to establish an accurate calculation method for the deflection caused by the effect of pre-stress in a steel truss web–concrete composite girder bridge based on the energy variational principle, considering the influence of shear deformation and the shear lag effect of the steel truss web member on the accuracy of the deflection calculation. The pre-stress effect is determined by the equivalent load method, and the deflection analytical solution for a composite girder bridge under straight-line, broken-line, and curve pre-stressing tendon arrangements is established. The reliability of the formula is verified using ANSYS 2022 finite element numerical simulation. At the same time, the influence of shear deformation, the shear lag effect, and their combined (dual) effect on the deflection calculation accuracy is analyzed under different linear pre-stressed reinforcement arrangements and comprehensive arrangements of pre-stressed reinforcement. The analysis of the example shows that the analytical solution for the deflection of the steel truss web– concrete composite beam, when considering only the shear deformation and the dual effect, is more consistent with the finite element numerical solution. The shear deformation of the steel truss web member under the eccentric straight-line arrangement alone does not cause additional deflection, and the additional deflection caused by the shear lag effect can be ignored. The influence of shear deformation on deflection is higher than that of the shear lag effect. The contribution ratio of the additional deflection caused by the dual effect is greater than 14%, and the influence of the dual effect on deflection is more obvious under a broken-line arrangement. Under the comprehensive arrangement of pre-stressing tendons, the contribution rate of shear deformation to the total deflection is about 3.5 times that of shear lag. Compared with the deflection value of the primary beam, the mid-span deflection is increased by 3.0%, 11.0%, and 13.9% when only considering the shear lag effect, only considering shear deformation, and considering the dual effect, respectively. Therefore, shear deformation and the shear lag effect should be considered when calculating the camber of a steel truss web–concrete composite girder bridge to improve the calculation accuracy....
Fissured expansive soils exhibit pronounced moisture-induced swelling, posing significant risks to the stability of geotechnical structures such as dam foundations and core zones. To improve predictive capacity in such environments, this study developed a back-propagation (BP) neural network model to estimate the swelling behavior of fissured expansive soils. The model incorporated four key geotechnical parameters—fissure ratio, dry density, initial moisture content, and overburden pressure—and was implemented in MATLAB using a three-layer feedforward architecture with four inputs, five hidden neurons, and a single output neuron to predict the swelling ratio (increase in specimen height due to water-induced expansion). The model was trained on 81 laboratory-tested samples, with all variables normalized to the range [−1, 1] to ensure numerical stability. Two training algorithms were evaluated: gradient descent with momentum (traingdm) and the Fletcher–Reeves conjugate gradient method (traincgf). The optimal network configuration achieved a mean squared error (MSE) below 0.01, indicating strong predictive accuracy for expansive soil swelling behavior. Comparative results showed that the conjugate gradient algorithm converged nearly 30 times faster than the gradient descent method, while maintaining similar prediction accuracy. Validation on an independent dataset confirmed high agreement with measured swelling ratios. The proposed BP model demonstrates robust generalization and computational efficiency, offering a practical decision-support tool for expansive soil deformation control in dam engineering. Its rapid and accurate predictions make it valuable for Smart City applications such as embankment stabilization, intelligent dam core design, and real-time geotechnical risk assessment....
Loading....