Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction

In recent years crushing waste brick to produce recycled brick aggregates (RBAs) has become a viable solution for reducing environmental pollution and addressing the natural resource shortage in civil engineering. To promote the widespread use of the recycled brick aggregate concrete (RBAC) in const...

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Main Authors: Lang Lin, Jinjun Xu, Jialiang Yuan, Yong Yu
Format: Article
Language:English
Published: Elsevier 2023-07-01
Series:Case Studies in Construction Materials
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2214509523003649
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author Lang Lin
Jinjun Xu
Jialiang Yuan
Yong Yu
author_facet Lang Lin
Jinjun Xu
Jialiang Yuan
Yong Yu
author_sort Lang Lin
collection DOAJ
description In recent years crushing waste brick to produce recycled brick aggregates (RBAs) has become a viable solution for reducing environmental pollution and addressing the natural resource shortage in civil engineering. To promote the widespread use of the recycled brick aggregate concrete (RBAC) in construction, this study analyzes existing test results on the attributes of RBAs and the compressive mechanical behaviors of RBAC. The review results indicate significant differences and variabilities in the characteristics of RBAs compared to natural coarse aggregates and recycled concrete coarse aggregates. RBAs have the highest absorption capacity and crushing index among the three aggregates, leading to changes in the compressive failure mechanism and a decline in the mechanical properties of RBAC. Additionally, it is also observed that existing formulas do not adequately account for the deterioration of the compressive mechanical properties of RBAC. To tackle this problem, artificial intelligence (AI) approaches including artificial neural network and multigene genetic programming are utilized to develop precise models for predicting the compressive strength and elastic modulus of RBAC. It is found that RBAC’s these two mechanical indexes are mainly influenced by the standard strength of cement paste, water-to-cement ratio, sand-to-aggregate mass ratio, RBA replacement ratio and mass-weighted water absorption ratio of coarse aggregates. The AI models developed in this study accurately capture the trends of these factors and offer desirable predictive results.
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spelling doaj.art-682be90b4edf4fcabaf828efb92e5ebb2023-06-21T06:54:50ZengElsevierCase Studies in Construction Materials2214-50952023-07-0118e02184Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based predictionLang Lin0Jinjun Xu1Jialiang Yuan2Yong Yu3State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510641, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing, Jiangsu Province 211816, ChinaGuangzhou City Construction & Development Co. Ltd, Guangzhou 510641, ChinaSchool of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China; Corresponding author.In recent years crushing waste brick to produce recycled brick aggregates (RBAs) has become a viable solution for reducing environmental pollution and addressing the natural resource shortage in civil engineering. To promote the widespread use of the recycled brick aggregate concrete (RBAC) in construction, this study analyzes existing test results on the attributes of RBAs and the compressive mechanical behaviors of RBAC. The review results indicate significant differences and variabilities in the characteristics of RBAs compared to natural coarse aggregates and recycled concrete coarse aggregates. RBAs have the highest absorption capacity and crushing index among the three aggregates, leading to changes in the compressive failure mechanism and a decline in the mechanical properties of RBAC. Additionally, it is also observed that existing formulas do not adequately account for the deterioration of the compressive mechanical properties of RBAC. To tackle this problem, artificial intelligence (AI) approaches including artificial neural network and multigene genetic programming are utilized to develop precise models for predicting the compressive strength and elastic modulus of RBAC. It is found that RBAC’s these two mechanical indexes are mainly influenced by the standard strength of cement paste, water-to-cement ratio, sand-to-aggregate mass ratio, RBA replacement ratio and mass-weighted water absorption ratio of coarse aggregates. The AI models developed in this study accurately capture the trends of these factors and offer desirable predictive results.http://www.sciencedirect.com/science/article/pii/S2214509523003649Recycled brick aggregate concrete (RBAC)Compressive strengthElastic modulusArtificial neural networkMultigene genetic programming
spellingShingle Lang Lin
Jinjun Xu
Jialiang Yuan
Yong Yu
Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
Case Studies in Construction Materials
Recycled brick aggregate concrete (RBAC)
Compressive strength
Elastic modulus
Artificial neural network
Multigene genetic programming
title Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
title_full Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
title_fullStr Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
title_full_unstemmed Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
title_short Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
title_sort compressive strength and elastic modulus of rbac an analysis of existing data and an artificial intelligence based prediction
topic Recycled brick aggregate concrete (RBAC)
Compressive strength
Elastic modulus
Artificial neural network
Multigene genetic programming
url http://www.sciencedirect.com/science/article/pii/S2214509523003649
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AT jinjunxu compressivestrengthandelasticmodulusofrbacananalysisofexistingdataandanartificialintelligencebasedprediction
AT jialiangyuan compressivestrengthandelasticmodulusofrbacananalysisofexistingdataandanartificialintelligencebasedprediction
AT yongyu compressivestrengthandelasticmodulusofrbacananalysisofexistingdataandanartificialintelligencebasedprediction