Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks

Dantas, Adriana Trocoli Abdon; Leite, Mônica Batista; Nagahama, Koji de Jesus

doi:10.1016/j.conbuildmat.2012.09.026

Cited by 242 publications

(76 citation statements)

References 19 publications

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“…Furthermore, some of the more sophisticated approaches to this problem fail to guarantee that the models proposed make physical sense. Nonetheless, there are very interesting new approaches to predict the mechanical properties of concrete with RA using sophisticated statistical models and tools, such as artificial neural networks (Dantas, Leite, & Nagahama, 2013; Duan, Kou, & Poon, 2013;Kim, Lee, et al, 2013;Topçu & Sarıdemir, 2008), fuzzy logic (Topçu & Sarıdemir, 2008) and non-linear and multi-linear regressing analysis Tam, Wang, & Tam, 2008;Younis & Pilakoutas, 2013). However, for the reasons stated above, the use of such methods was not the strategy followed in the research presented here.…”

Section: Aggregate Classmentioning

confidence: 90%

The influence of the use of recycled aggregates on the compressive strength of concrete: a review

Silva

Brito

Dhir

2014

European Journal of Environmental and Civil Engineering

265

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This paper provides a systematic review of 119 publications, selected from 235, published over a period of 36 years from 1978 to 2014, relating to the effect on concrete compressive strength of the various aspects related to the use of recycled aggregates (RA) such as replacement level, size, origin, moisture content, exposure of the resulting concrete to different environmental conditions, use of chemical admixtures and additions, and strength development over time. The data were collectively subjected to a statistical analysis, the results of which allowed producing a model for predicting concrete strength, based on the quality and content of the RA.

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Section: Aggregate Classmentioning

confidence: 90%

The influence of the use of recycled aggregates on the compressive strength of concrete: a review

Silva

Brito

Dhir

2014

European Journal of Environmental and Civil Engineering

265

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“…Data were collected from ground penetrating radar (GPR) electrical resistivity and ultrasonic pulse velocity. Dantas et al [9] developed ANNs model for predicting the compressive strength of concrete containing Construction and Demolition Waste. Atıcı [10] used multiple regression analysis and an artificial neural network for estimating compressive strength of mineral-admixtured concrete using data collected from non-destructive testing rebound number and ultrasonic pulse velocity.…”

Section: Introductionmentioning

confidence: 99%

Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

Ayaz

Kocamaz

Karakoç

2015

Construction and Building Materials

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“…Although such soft-computing techniques as artificial neural networks (ANNs), genetic programming (GP) and adaptive neuro-fuzzy interfacial systems (ANFIS) have been successfully applied to a wide range of civil engineering problems so far [19][20][21][22][23][24][25][26][27][28][29][30], this is very limited in geopolymers field as a new type highperformance construction materials and is only limited to the previous works (see [31][32][33] for example). In the previous work [31], compressive strength of geopolymers with different aluminosilicate source was modeled by ANNs.…”

Section: Introductionmentioning

confidence: 99%

Compressive strength of tungsten mine waste- and metakaolin-based geopolymers

Nazari

Pacheco-Torgal

Çevik

2014

Ceramics International

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Neuro-fuzzy approach has been successfully applied to a wide range of civil engineering problems so far. However, this is limited for geopolymeric specimens. In the present study, compressive strength of different types of geopolymers has been modeled by adaptive neuro-fuzzy interfacial systems (ANFIS). The model was constructed by 395 experimental data collected from the literature and divided into 80% and 20% for training and testing phases, respectively. Curing time, Ca(OH) 2 content, NaOH concentration, mold type, aluminosilicate source and H 2 O/Na 2 O molar ratio were independent input parameters in the proposed model. Absolute fraction of variance, absolute percentage error and root mean square error of 0.94, 11.52 and 14.48, respectively in training phase and 0.92, 15.89 and 23.69, respectively in testing phase of the model were achieved showing the relatively high accuracy of the proposed ANFIS model. By the obtained results, a comparative study was performed to show the interaction of some selected factors on the compressive strength of the considered geopolymers. The discussions findings were in accordance to the experimental studies and those results presented in the literature.

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Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks

Cited by 242 publications

References 19 publications

The influence of the use of recycled aggregates on the compressive strength of concrete: a review

The influence of the use of recycled aggregates on the compressive strength of concrete: a review

Modeling of compressive strength and UPV of high-volume mineral-admixtured concrete using rule-based M5 rule and tree model M5P classifiers

Compressive strength of tungsten mine waste- and metakaolin-based geopolymers

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