The shear strength prediction model for fiber-reinforced polymer (FRP) bar-reinforced concrete beams without stirrups in ACI440.1R-2015 does not consider the “size effect” and the effect of shear span-to-depth ratio and predicts the zero-shear strength for concrete members without longitudinal reinforcement. A modified shear strength prediction model for FRP bar-reinforced concrete beams without stirrups was presented in this paper. The proposed model takes into account the effect of concrete strength, size of the beam, shear span-to-depth ratio, reinforcement ratio, and modulus of elasticity of the longitudinal reinforcement and the “size effect.” The superiority of the proposed model has been evaluated by comparing the calculated shear strength of FRP bar-reinforced concrete beams without stirrups by the proposed model with the experimental results and calculated values by the models in design codes, respectively. It confirmed that the shear strength of FRP bar-reinforced concrete beams without stirrups by the proposed model was in better agreement with the experimental results.

The shear failure of a reinforced concrete beam generally occurs when the principal tensile stress near the neutral axis is equal to or greater than the tension strength of concrete. In order to set up a model for shear strength for FRP bar reinforced concrete beams without stirrups by the mechanical method, this paper equivalently transformed the FRP bar reinforced concrete rectangular beam with cracks as one composed of ideal elastic material to facilitate the analysis and proposed a new and more reasonable model of shear strength for FRP bar reinforced concrete beams without stirrups. Then, an experimental database including 235 FRP bar reinforced beams without stirrups was compiled to verify the validity of the proposed model. It was found that the values from the proposed model are in better agreement with the experimental results of shear strength of FRP bar reinforced concrete beams without stirrups in comparison with the models in codes.

Precipitation is a vital component of the regional water resource circulation system. Accurate and efficient precipitation prediction is especially important in the context of global warming, as it can help explore the regional precipitation pattern and promote comprehensive water resource utilization. However, due to the influence of many factors, the precipitation process exhibits significant stochasticity, uncertainty, and nonlinearity despite having some regularity. In this article, monthly precipitation in Zhoukou City is predicted using a complementary ensemble empirical modal decomposition (CEEMD) method combined with a long short-term memory neural network (LSTM) model and a least squares support vector machine (LSSVM) model. The results demonstrate that the CEEMD-LSTM-LSSVM model exhibits a root mean square error of 15.01 and a mean absolute error of 11.31 in predicting monthly precipitation in Zhoukou City. The model effectively overcomes the problems of modal confounding present in empirical modal decomposition (EMD), the existence of reconstruction errors in ensemble empirical modal decomposition (EEMD), and the lack of accuracy of a single LSTM model in predicting modal components with different frequencies obtained by EEMD decomposition. The model provides an effective approach for predicting future precipitation in the Zhoukou area and predicts monthly precipitation in the study area from 2023 to 2025. The study provides a reference for relevant departments to take effective measures against natural disasters and rationally plan urban water resources.

In this study, the water standard is interval rather than point, so a new analysis method which proposed from the grey system theory - Grey Relational Analysis (GRA) based on the distance of a point to the interval, is used for assessment of drinking water quality in Jiaozuo city China; This method is simple and has good operability, and the physics significance was clear. Good results are achieved. The assessment results provide a scientific reference for the integrated planning, management and rational use of drinking water source in Jiaozuo city.