A. Alyaseen, Arunava Poddar, Hussain Alahmad, Navsal Kumar, P. Sihag
{"title":"再生粗骨料高性能自密实混凝土:配合比设计参数的综合系统综述","authors":"A. Alyaseen, Arunava Poddar, Hussain Alahmad, Navsal Kumar, P. Sihag","doi":"10.1080/24705314.2023.2211850","DOIUrl":null,"url":null,"abstract":"ABSTRACT The technological advancements and environmental concerns enlighten the importance of incorporating more high-performance engineered materials in the construction sector. The partial replacement of natural coarse aggregates (NCA) with recycled coarse aggregates (RCA) in concrete has recently been a primary focus of worldwide researchers for sustainability in environmental aspects. The primary purpose of this review is to comprehend the effect of design parameters in determining the mechanical characteristics of high-performance self-compacting (HP-SCC) that include recycled coarse aggregates (RCA). Seven design parameters were extracted and considered in this review. It has been revealed that the design parameters of HP-SCC with RCA have a different effect on the mechanical characteristics of HP-SCC with various grades. In addition, the current research aims to promote environmental-friendly development and produce sustainable materials to improve mechanical-related characteristics in concrete in the absence of a precise evaluation technique. Artificial neural network (ANN) models have been implemented using the design parameters for predicting concrete mechanical properties based on three statistical indicators. The ANN-based model was attributed using these seven inputs of the literature with the help of sensitivity analysis for indicating the most critical design parameter HP-SCC.","PeriodicalId":43844,"journal":{"name":"Journal of Structural Integrity and Maintenance","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"High-performance self-compacting concrete with recycled coarse aggregate: comprehensive systematic review on mix design parameters\",\"authors\":\"A. Alyaseen, Arunava Poddar, Hussain Alahmad, Navsal Kumar, P. Sihag\",\"doi\":\"10.1080/24705314.2023.2211850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT The technological advancements and environmental concerns enlighten the importance of incorporating more high-performance engineered materials in the construction sector. The partial replacement of natural coarse aggregates (NCA) with recycled coarse aggregates (RCA) in concrete has recently been a primary focus of worldwide researchers for sustainability in environmental aspects. The primary purpose of this review is to comprehend the effect of design parameters in determining the mechanical characteristics of high-performance self-compacting (HP-SCC) that include recycled coarse aggregates (RCA). Seven design parameters were extracted and considered in this review. It has been revealed that the design parameters of HP-SCC with RCA have a different effect on the mechanical characteristics of HP-SCC with various grades. In addition, the current research aims to promote environmental-friendly development and produce sustainable materials to improve mechanical-related characteristics in concrete in the absence of a precise evaluation technique. Artificial neural network (ANN) models have been implemented using the design parameters for predicting concrete mechanical properties based on three statistical indicators. The ANN-based model was attributed using these seven inputs of the literature with the help of sensitivity analysis for indicating the most critical design parameter HP-SCC.\",\"PeriodicalId\":43844,\"journal\":{\"name\":\"Journal of Structural Integrity and Maintenance\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Structural Integrity and Maintenance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/24705314.2023.2211850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Structural Integrity and Maintenance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/24705314.2023.2211850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
High-performance self-compacting concrete with recycled coarse aggregate: comprehensive systematic review on mix design parameters
ABSTRACT The technological advancements and environmental concerns enlighten the importance of incorporating more high-performance engineered materials in the construction sector. The partial replacement of natural coarse aggregates (NCA) with recycled coarse aggregates (RCA) in concrete has recently been a primary focus of worldwide researchers for sustainability in environmental aspects. The primary purpose of this review is to comprehend the effect of design parameters in determining the mechanical characteristics of high-performance self-compacting (HP-SCC) that include recycled coarse aggregates (RCA). Seven design parameters were extracted and considered in this review. It has been revealed that the design parameters of HP-SCC with RCA have a different effect on the mechanical characteristics of HP-SCC with various grades. In addition, the current research aims to promote environmental-friendly development and produce sustainable materials to improve mechanical-related characteristics in concrete in the absence of a precise evaluation technique. Artificial neural network (ANN) models have been implemented using the design parameters for predicting concrete mechanical properties based on three statistical indicators. The ANN-based model was attributed using these seven inputs of the literature with the help of sensitivity analysis for indicating the most critical design parameter HP-SCC.