English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This study, proposes the use of a novel rubber-sand concrete (RSC) material, which comprises rubber particles, sand, and cement, as an aseismic material in practical engineering construction. The uniaxial compressive strength (UCS) of damping materials is an important factor that directly affects the seismic activity in underground structures. To predict the UCS of RSC, artificial intelligence model back propagation neural network (BPNN), which is optimized through four swarm intelligence optimization (SIO) algorithms: particle swarm optimization algorithm (PSO), fruit fly optimization algorithm (FOA), lion swarm optimization algorithm (LSO), and sparrow search algorithm (SSA), is used. The dataset for the prediction models was obtained from uniaxial compression tests in the RSC laboratory. The performances of the four hybrid intelligence models were evaluated using six performance indicators: the root mean square error ( RMSE ), correlation coefficient ( R ), determination coefficient ( R2 ), mean absolute error ( MAE ), mean square error ( MSE ), and sum of square error ( SSE ).The prediction capability of these models was graded based on these indicators using a ranking system. The results show that the prediction ability of the LSO-BPNN hybrid model is better than that of the three other hybrid models, with  RMSE  of (1.0635, 1.2352),  R  of (0.9887, 0.9713),  R 2of (0.9776, 0.9165),  MAE  of (0.7257, 0.8243),  MSE  of (1.1352, 1.5256),  SSE  of (64.7074, 36.6151), and ranking score of (24, 24) in the training and testing phases, respectively. Therefore, the LSO-BPNN hybrid model is an efficient and accurate method for predicting the UCS of RSCs. Sensitivity analysis showed that rubber and sand were the most important elements that affected UCS prediction, followed by cement, with the lowest relative importance being RPZ. This study provides guidance for the extension and application of RSC materials to underground seismic engineering.

mechanics of advanced materials and structures

Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete