Predicting the durability and strength of fiber-reinforced concrete exposed to salt and freezing conditions
This study aims to determine the influence of salt-freezing conditions on the durability of basalt fiber fine stone concrete and accurately predict strength changes considering nonlinear characteristics and external factors.Hydraulic structures and their environmental conditions in the saline-alkali land of Jingdian irrigation area in Gansu,China served as a test case.Indoor material tests were conducted by varying the freezing and thawing medium(clean water,3%NaCl solution,5%Na2SO4 solution)and basalt fiber content(0,0.05%,0.10%,0.15%,0.20%).This provided preliminary insights into uniaxial compressive strength changes of basalt fiber fine stone concrete under different salt-freeze environments.Based on laboratory results,a basic-BP model combining BPNN and the beetle antennae search algorithm(BAS)was developed to predict compressive strength changes considering varying salt-freezing conditions.Additionally,two other BPNN models improved by intelligent algorithms were constructed for comparison.Model performance and error analysis revealed the BAS-BP model predictions agreed closely with tests,demonstrating good accuracy and stability.This can greatly improve efficiency in obtaining durability test results for basalt fiber fine stone concrete.Appropriate basalt fiber content,such as 0.15%,was found to enhance salt freezing resistance,with optimal performance across factors.NaCl exposure caused more severe damage than Na2SO4 during freezing and thawing.The error analysis revealed that the BAS-BP model's predictions most closely matched the test results,demonstrating strong predictive accuracy and stability.
fine stone concretebasalt fibercompressive strengthneural networksBeetle Antennae Search(BAS)
NETL
NSTL
万方数据
维普
