Pervious concrete is recommended,which is of great benefit to the ecological environment and human living environment.In this paper,the influences of five water-cement ratios and four fly ash contents to replace the c...Pervious concrete is recommended,which is of great benefit to the ecological environment and human living environment.In this paper,the influences of five water-cement ratios and four fly ash contents to replace the cement by mass with a water-cement ratio of 0.30 on the properties of Recycled Aggregate Pervious Concrete(RAPC)were studied.Following this,based on the Grey relational-Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)optimization method,the strength,permeability,abrasion loss rate,and material costs of RAPC were adopted as evaluation indices to establish a mix proportion optimization model.The results show that the increase of water-cement ratio and fly ash replacement level of RAPC leads to decreased compres-sive strength while an increase in the permeability and abrasion loss rate.According to test results based on the optimal model 0.30 was identified as the best mix proportion.In addition,ecological-economic analysis of RAPC raw materials was carried out by comparing different natural aggregates.The results of EE(embodied energy)and ECO 2e(embodied CO_(2) emission)pointed out that the combination of recycled aggregate and fly ash leads to sig-nificant ecological and economic benefits.展开更多
The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum.The sparrow search algorithm(SSA)was improved as ISSA using both methods of nonlinear Sine Cosine optimi...The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum.The sparrow search algorithm(SSA)was improved as ISSA using both methods of nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio.We combined the Bidirectional Long Short-Term Memory(BiLSTM)neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model.The critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction.The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations,achieving an R^(2) of 0.987.In comparison to SSABiLSTM and BiLSTM,the model exhibits a substantial reduction in errors,decreasing by approximately 20%and 30%,respectively.This model can provide technical support and insights for accurately predicting dam seepage,contributing to the advancement of this field.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.52009045)National Key Research and Development Program of China(No.2018YFC0406902).
文摘Pervious concrete is recommended,which is of great benefit to the ecological environment and human living environment.In this paper,the influences of five water-cement ratios and four fly ash contents to replace the cement by mass with a water-cement ratio of 0.30 on the properties of Recycled Aggregate Pervious Concrete(RAPC)were studied.Following this,based on the Grey relational-Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)optimization method,the strength,permeability,abrasion loss rate,and material costs of RAPC were adopted as evaluation indices to establish a mix proportion optimization model.The results show that the increase of water-cement ratio and fly ash replacement level of RAPC leads to decreased compres-sive strength while an increase in the permeability and abrasion loss rate.According to test results based on the optimal model 0.30 was identified as the best mix proportion.In addition,ecological-economic analysis of RAPC raw materials was carried out by comparing different natural aggregates.The results of EE(embodied energy)and ECO 2e(embodied CO_(2) emission)pointed out that the combination of recycled aggregate and fly ash leads to sig-nificant ecological and economic benefits.
基金supported by the North China University of Water Resources and Electric Powersupport of The National Natural Science Foundation of China(51979169)Henan Province Innovation Talent Support Plan(24HASTIT017).
文摘The existing traditional dam seepage pressure prediction models have problems such as falling into local optimum.The sparrow search algorithm(SSA)was improved as ISSA using both methods of nonlinear Sine Cosine optimization algorithm and adaptive producer and scrounger ratio.We combined the Bidirectional Long Short-Term Memory(BiLSTM)neural network model with ISSA to develop the ISSA-BiLSTM seepage pressure prediction model.The critical feature factors were extracted based on LightGBM to construct the input layer for seepage pressure prediction.The results show that the ISSA-BiLSTM model's fitting outcomes are generally consistent with the observed changes in seepage pressure observations,achieving an R^(2) of 0.987.In comparison to SSABiLSTM and BiLSTM,the model exhibits a substantial reduction in errors,decreasing by approximately 20%and 30%,respectively.This model can provide technical support and insights for accurately predicting dam seepage,contributing to the advancement of this field.
