Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks

Xu, Jinjun; Zhao, Xinyu; Yong, Yu; Xie, Tianyu; Yang, Guosong; Xue, Jianyang

doi:10.1016/j.conbuildmat.2019.03.234

Cited by 190 publications

(53 citation statements)

References 45 publications

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“…Prior to conducting the ML-based analysis, it is useful to identify the importance of various parameters. Such a rehearsal can provide insights into whether the selected input parameters are appropriate [ 12 , 13 ]. To achieve this objective, the powerful mathematical tool of Grey relational analysis (GRA), which has been proposed to deal with poor, incomplete, and uncertain systems [ 76 ], was used in this study.…”

Section: Parametric Importance Evaluation Using Grey Relational Anmentioning

confidence: 99%

“…One of the biggest concerns is the inferior properties of RA, which would result in significant reductions in the mechanical properties of RAC [ 4 , 5 , 6 , 7 ]. This should impede the acceptance of RAC-based members, though many laboratory studies have revealed their structural feasibility [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Another wave of concern is around design and modeling, where the validity of previous code-based models and analytical procedures developed for natural aggregate concrete (NAC) components is questioned for their RAC counterparts; this issue has begun to be systematically addressed [ 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Yong

Zhao

et al. 2020

Materials

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Recycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application scenarios where brittle failure is possible (e.g., beam in shear). In this paper, existing major shear design formulae established primarily for conventional concrete beams were assessed for RAC beams. Results showed that when applied to the shear test database compiled for RAC beams, those formulae provided only inaccurate estimations with surprisingly large scatter. To cope with this bias, machine learning (ML) techniques deemed as potential alternative predictors were resorted to. First, a Grey Relational Analysis (GRA) was carried out to rank the importance of the parameters that would affect the shear capacity of RAC beams. Then, two contemporary ML approaches, namely, the artificial neural network (ANN) and the random forest (RF), were leveraged to simulate the beams’ shear strength. It was found that both models produced even better predictions than the evaluated formulae. With this superiority, a parametric study was undertaken to observe the trends of how the parameters played roles in influencing the shear resistance of RAC beams. The findings indicated that, though less influential than the structural parameters such as shear span ratio, the effect of the replacement ratio of recycled aggregate (RA) was still significant. Nevertheless, the value of vc/(fc)1/2 (i.e., the shear contribution from RAC normalized with respect to the square root of its strength) predicted by the ML-based approaches appeared to be insignificantly affected by the replacement level. Given the existing inevitable large experimental scatter, more shear tests are certainly needed and, for safe application of RAC, using partial factors calibrated to consider the uncertainty is feasible when designing the shear strength of RAC beams. Some suggestions for future works are also given at the end of this paper.

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Section: Parametric Importance Evaluation Using Grey Relational Anmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Yong

Zhao

et al. 2020

Materials

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“…Recycled concrete aggregates (RCAs) generated from construction and demolition waste (CDW) have been deemed as a potential alternative to natural aggregates (NAs) with the advantage of minimizing the environmental impacts of CDW [1][2][3][4], where the resulting concrete products, termed recycled aggregate concrete (RAC), are now received as a type of "green concrete" [5][6][7][8][9][10]. Along this line, the reinforced recycled concrete (RRC) members (i.e., reinforced concrete slabs, beams, columns and shear walls manufactured with RCAs) have been developed and explored to promote the sustainable use of RAC at the structural level.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings

et al. 2019

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Recycled concrete aggregates (RCAs) generated from construction and demolition activities have been recognized as a feasible alternative to natural aggregates (NAs). Naturally, the columns fabricated with reinforced recycled concrete (RRC) have been proposed and investigated to promote the structural use of recycled aggregate concrete (RAC). There is still, however, very limited modeling research available to reproduce, accurately and efficiently, the seismic response of RRC columns under lateral cyclic loading; proper evaluations are also lacking on addressing the columns’ seismic behaviors. To fill some of those research gaps, a fiber-based numerical model is developed in this study and then validated with the experimental results published in the literature. Subsequently, the numerical model justified is applied to carry out a comprehensive parametric study to examine the effects of a range of variables on the hysteretic characteristics of RRC columns. Furthermore, a grey relational analysis is conducted to establish quantifiable evidence of key variable sensitivities. The evaluation results imply that the use of the additional water method (AWM) for manufacturing RAC is likely to reduce the lateral load-carrying capacity of the RRC columns (up to 10%), whereas the opposite would occur if a conventional mixing procedure is adopted. Moreover, compared with other factors such as steel area ratio, the content of RCA replacement has a less remarkable effect on the seismic performance of the RRC columns. In general, the RRC columns possess acceptable seismic-resistant properties, and they can be used in earthquake-prone regions with confidence.

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“…GST has been applied in many fields due to its good characteristics and continuous development by many scholars. These fields include: optimal plan selection for energy planning [43][44][45], optimal supplier selection in supply chains [46], traffic safety decisions for ship navigation maneuvers [47], reasonable distribution and prediction of urban heating [48,49], performance prediction for concrete materials [50][51][52], assessment of regional agricultural water and soil resources system [53], and comprehensive evaluation of the effectiveness of coal-fired power plants [54]. GRA has made remarkable progress in these applications and the results are reliable and accurate.…”

Section: Improvement Of Traditional Variable Fuzzy Setmentioning

confidence: 99%

Evaluation of Dam Break Social Impact Assessments Based on an Improved Variable Fuzzy Set Model

He¹,

Jaturapitakkul²,

Qin³

et al. 2020

Water

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In recent years attention has shifted from “dam safety” to “dam risk” due to the high loss characteristics of dam breaks. Despite this, there has been little research on social impact assessments. Variable fuzzy sets (VFSs) are a theoretical system for dealing with uncertainty that are used in many industries. However, the relative membership degree (RMD) calculations required for VFSs are complicated and data can be overlooked. Furthermore, the RMD is highly subjective when dealing with qualitative problems, which can seriously affect the accuracy of the results. This study introduces grey system theory (GST) which analyzes the RMD characteristics to improve traditional VFSs. A new method for calculating the social impact of a dam break is proposed based on the correlation between the core parameters of the two theories. The Liujiatai Reservoir is used as a test case and the new and traditional evaluation methods are compared. The results show that the proposed method has advantages when dealing with uncertainty that are consistent with the characteristics of the problems associated with dam break social impact assessments. Moreover, the evaluation results obtained using the proposed method are consistent with, or more accurate than, those obtained using the traditional method.

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Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks

Cited by 190 publications

References 45 publications

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings

Evaluation of Dam Break Social Impact Assessments Based on an Improved Variable Fuzzy Set Model

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