文献类型：期刊文献

英文题名：Intelligence decision mechanism for prediction of compressive strength of self-compaction green concrete via neural network

作者：Jiang, Haidong Liu, Guoliang Alyami, Hashem Alharbi, Abdullah Jameel, Mohammed Khadimallah, Mohamed Amine

第一作者：Jiang, Haidong;江海东

通信作者：Liu, GL[1]

机构：[1]Guizhou Inst Technol, Sch Resources & Environm Engn, Guiyang 550003, Guizhou, Peoples R China;[2]Tongji Univ, Dept Geotech Engn, Minist Educ, Key Lab Geotech & Underground Engn, Shanghai 200092, Peoples R China;[3]Changshu Inst Technol, Sch Text Garment & Design, Changshu 215500, Jiangsu, Peoples R China;[4]Taif Univ, Coll Comp & Informat Technol, Dept Comp Sci, POB 11099, At Taif 21944, Saudi Arabia;[5]Taif Univ, Coll Comp & Informat Technol, Dept Informat Technol, POB 11099, At Taif 21944, Saudi Arabia;[6]King Khalid Univ, Coll Engn, Dept Civil Engn, Abha, Saudi Arabia;[7]Prince Sattam Bin Abdulaziz Univ, Coll Engn Civil Engn Dept, Al Kharj 16273, Saudi Arabia;[8]Univ Carthage, Polytech Sch Tunisia, Lab Syst & Appl Mech, Tunis, Tunisia

第一机构：贵州理工学院资源与环境工程学院

通信机构：corresponding author), Changshu Inst Technol, Sch Text Garment & Design, Changshu 215500, Jiangsu, Peoples R China.

年份：2022

卷号：340

外文期刊名：JOURNAL OF CLEANER PRODUCTION

收录：;EI(收录号：20220711631598);Scopus(收录号：2-s2.0-85124379000);WOS:【SCI-EXPANDED(收录号:WOS:000774192800001)】；

基金：This work was supported by the Research on the application of Bim in the whole life cycle of Urban Rail Transit (Foundation of Guizhou science and technology cooperation [2019] No. 1420). The special projects for promoting the development of big data of Guizhou Institute of Technology. And that is also funded by Geological Resources and Geological Engineering, Guizhou Provincial Key Disciplines, China (ZDXK[2018]001).

语种：英文

外文关键词：Artificial intelligence algorithms; Neural network; Rice husk ash; Calcium carbide waste; Compressive strength; Self-compacting green concrete

摘要：Civil engineering has a specific position for different forms of concrete as a century-old material. One of the most popular materials for human consumption is made from this substance. In recent years, solutions have been presented for the manufacturing of concrete, making concrete environmentally friendly and also making current waste useable as additives for concrete. In this research, the mechanical features of self-compacting green concrete (SCGC) comprising of compressive, tensile and flexural strength have been studied while diverse amounts of calcium carbide waste (CCW) and rice husk ash (RHA) were tested as partial cement replacements at 3, 7 and 28 days of the curing age. Then, according to available experimental results, artificial intelligence algorithms including Emotional Neural Network-chaotic particle swarm optimization (EANN-CPSO), First-principles molecular-dynamics (FPMD), and conventional Linear Regression (LR) model were applied for predicting the mechanical features of self-compacting concrete (SCC) while adding up to 10% RHA and up to 20% CCW in the SCGC blends. It was reported that the addition of CCW decreases the workability of SCGC mixes and raises the compressive strength (CS) at 28 days for SCGC mixes including 10% of RHA and without CCW compared to control mixes. Also, in both the testing and training phases, the minimum R-2 value FPMD, EANN-CPSO and LR models is about 0.904. It was found that the hybrid models of EANN-CPSO-S3, FPMD -S3 and LR-S3 show the most accurate performance with R-2 = 0.997 and 0.970 for EANN-CPSO-S3, R-2 = 0.967 and 0.954 for FPMD -S3 and R-2 = 0.934 and 0.929 for LR-S3 in both phases. The result shows that additional models might increase the performance of the model, such as new algorithms, hybrid models and optimization approaches.