Fracture behavior of a sustainable material: Recycled concrete with waste crumb rubber subjected to elevated temperatures

Tang, Yunchao; Feng, Wanhui; Chen, Zheng; Nong, Yumei; Guan, Shuhong; Sun, Junbo

doi:10.1016/j.jclepro.2021.128553

Cited by 95 publications

(27 citation statements)

References 55 publications

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“…Their findings reveal that combining gypsum-cement binder and TPP waste increases the physical and mechanical characteristics. Tang et al [ 31 ] examined the fracture behavior of rubber modified recycled aggregated concrete (RRAC) at varying temperatures (200, 400 and 600) °C. The finding of their study shows that the rubber aggregates have greater unstable fracture toughness than the initial cracking toughness of recycled aggregate concrete after exposing to elevated temperature.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

et al. 2021

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To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.

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Section: Introductionmentioning

confidence: 99%

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

et al. 2021

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“…Sensing technologies are also employed in the experimental investigation on rehabilitation of corroded reinforced concrete columns [ 8 , 9 , 10 ]. For structure built with new cement materials such as solid wastes incorporated concrete, the structural monitoring is rather important [ 11 , 12 , 13 , 14 , 15 ]. By combining with artificial intelligence, the data derived from sensors can be efficiently and effectively analysed to guide the design the construction [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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Sensing technologies present great improvements in construction performance including the safety, productivity, and quality. However, the corresponding applications in real projects are far behind compared with the academically research. This research aims to discover dominate influence factors in the sensing technologies adoption and ultimately develop a governance framework facilitating adoption processes. The framework is dedicated on general sensing technologies rather than single sensor in previous framework studies. To begin with, the influence factors of sensing technologies and other similar emerging technologies are summarised through a review. Then, a mixed methods design was employed to collect quantitative data through an online survey, and qualitative data through semi-structured interviews. Findings of the quantitative method reveal that the most widely implemented sensing technologies are GPS and visual sensing technology, but they’re still not adopted by all construction companies. Partial Least Squares Structural Equation Modelling reveals that supplier characteristics have the highest effect in all influence factors. Qualitative method was adopted to investigate perceptions of construction stakeholders on the major decision-making considerations in the adoption process. Ultimately, a triangulation analysis of findings from the literature review, online survey and interviews resulted in the governance framework development. The overarching contribution of this research focus on the general adoption of sensing technologies rather than the adoption of a specific sensor. Therefore, the governance framework can assist with the decision-making process of any sensing technology adoption in construction.

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“…Among most ML models, the back-propagation neural network (BPNN) demonstrates superior predicting capacity for solving engineering problems. The main reason is that BPNN is fast and easy to program without parameters to tune apart from the number of neurons in the hidden layer [54][55][56]. Therefore, BPNN is chosen as the prediction ML model in this study.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

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Fracture behavior of a sustainable material: Recycled concrete with waste crumb rubber subjected to elevated temperatures

Cited by 95 publications

References 55 publications

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

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