Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques

Xu, Jinjun; Chen, Yuliang; Xie, Tianyu; Zhao, Xinyu; Xiong, Baiqing; Chen, Zongping

doi:10.1016/j.conbuildmat.2019.07.155

Cited by 89 publications

(26 citation statements)

References 71 publications

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“…New concrete mixes and concepts are being developed across the world [41,42,44,[51][52][53][54] for meeting sustainable infrastructure demands or to attain novel properties, and few studies are reported for India as well [55][56][57]. This could even include fibre-reinforced concrete (both natural [58,59] and artificial: basalt nano-fibre [60]; steel fibre [56]), recycled aggregate concrete [61], and carbon nanotube reinforced concrete [62], among others. Fire and elevated temperature responses of these new materials, including thermal expansion, temperature profiles, deterioration, spalling, changes in strength and thermal properties with temperature, explosive spalling, post-event properties, etc., have been investigated internationally [63] but such data need to be carefully examined before such materials can be adopted for important infrastructure in India.…”

Section: Generation Of Fire/elevated Temperature Test Data For Indianmentioning

confidence: 99%

Fire Resistance and Elevated Temperature in Reinforced Concrete Members: Research Needs for India

Dauji

Kulkarni

2021

J. Inst. Eng. India Ser. A

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Fire and elevated temperature are important considerations for design of concrete structures, for both normal and nuclear facilities. In India, the present design guidelines for concrete subjected to fire and elevated temperature are limited to prescriptive minimum dimension and minimum cover. The other approach suggested is fire test based, and being resource intensive, is considered only for special cases of design. The various provisions of Indian standards are discussed and compared to the international standards. Fire test data for concrete/reinforcement properties at elevated temperature for Indian materials are scarce, and hence, the calculation methods are difficult to apply for Indian concrete. Validation of such methods for India is required, which again can only happen after generation of adequate database. The article highlights the present research needs for fire design of Indian concrete, along with development of analysis methodologies/guidelines for fire design of concrete structures, assessment of fire-affected concrete structures, and combination of the fire/temperature load with other extreme loads such as impact, seismic load, or missile load using advanced approaches. This article could serve as reference for designers as well as the starting point for several research initiatives in India and thereby is expected to improve the state of the art of fire design of concrete structures in India in the long term.

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Section: Generation Of Fire/elevated Temperature Test Data For Indianmentioning

confidence: 99%

Fire Resistance and Elevated Temperature in Reinforced Concrete Members: Research Needs for India

Dauji

Kulkarni

2021

J. Inst. Eng. India Ser. A

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“…The results show that results predicted by ANN were more accurate (Chithra et al, 2016). Xu et al (2019) used ANN to predict the performance of recycled aggregate concrete under triaxial loads. Zhou et al (2020) used ANN to predict the interface bond strength between fiber and concrete.…”

Section: Introductionmentioning

confidence: 98%

Optimum Design of High-Strength Concrete Mix Proportion for Crack Resistance Using Artificial Neural Networks and Genetic Algorithm

et al. 2020

Front. Mater.

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The fact that high-strength concrete is easily to crack has a significant negative impact on its durability and strength. This paper gives an optimum design method of high-strength concrete for improving crack resistance based on orthogonal test artificial neural networks (ANN) and genetic algorithm. First, orthogonal test is operated to determine the influence of the concrete mix proportion to the slump, compressive strength, tensile strength, and elastic modulus, followed by calculating and predicting the concrete performance using ANN. Based on results from orthogonal test and ANN, a functional relationship among slump, compressive strength, tensile strength, elastic modulus, and mix proportion has been built. On this basis, using the widely used shrinkage and creep models, the functional relationship between the concrete cracking risk coefficient and the mix proportion is derived, and finally genetic algorithm is used to optimize the concrete mix proportion to improve its crack resistance. The research results showed that, compared with the control concrete, the cracking risk coefficient of the optimized concrete was reduced by 25%, and its crack resistance was significantly improved.

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“…Duan et al (2018) analyzed each element or materials parameter as input by ANN and reduced the prediction error of ANN by adopting the 14-16-1 ANNs model. Xu et al (2019a, 2019b) used multiple non-linear regression (MNR), ANN and hybrid genetic algorithm artificial neural network (GA-ANN) to simulate mechanical triaxial loads. The performance results of recycled aggregate concrete (RAC) with ANN showed that that the developed MNR equation and neural network model can satisfactorily predict the behavior of RAC under triaxial load.…”

Section: Introductionmentioning

confidence: 99%

Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network

Singh

2021

Advances in Structural Engineering

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A back propagation (BP) neural network (NN) model was used to analyze the relationship between the cube compressive strength and various strength indicators of concrete with large-sized recycled aggregates (LSRA) (80 mm maximum size). Factors such as strength and replacement rate of recycled aggregates were used as input parameters to establish the neural network model. The BP-NN model was optimized by analyzing the influence and sensitivity of each parameter in the model. Then the mechanical properties of concrete with LSRA were predicted. Results showed that the strength of new concrete had a more significant impact on the strength of recycled concrete with LSRA, followed by the strength of old concrete. While considering all the factors, including the mechanical strength and the replacement ratio regarding the maximum utilization of RA, the 30% incorporation rate was suggested as an ideal incorporation rate.

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Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques

Cited by 89 publications

References 71 publications

Fire Resistance and Elevated Temperature in Reinforced Concrete Members: Research Needs for India

Fire Resistance and Elevated Temperature in Reinforced Concrete Members: Research Needs for India

Optimum Design of High-Strength Concrete Mix Proportion for Crack Resistance Using Artificial Neural Networks and Genetic Algorithm

Strength index analysis of concrete with large size recycled aggregate based on back propagation neural network

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