Estimation of compressive strength of stirrup‐confined circular columns using artificial neural networks

Chang, Wei; Zheng, Wenzhong

doi:10.1002/suco.201800259

Cited by 22 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many civil engineering problems have been solved using ML algorithms. These include geotechnical engineering [ 17 , 18 ], pavement structures [ 19 ], structural engineering [ 20 , 21 , 22 , 23 ], composite structural elements [ 24 ], material science [ 25 , 26 , 27 , 28 , 29 ], and traffic engineering [ 30 , 31 , 32 , 33 ]. Moreover, previous research has been conducted to predict the Dcl in concrete using an AI-based approach [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Fuhaid

Alanazi

2023

Materials

View full text Add to dashboard Cite

The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C3A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R2 values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Fuhaid

Alanazi

2023

Materials

View full text Add to dashboard Cite

show abstract

“…AI tools are more useful in predicting the behavior of concrete, and mostly the prediction using artificial neural network (ANN) is more efficient 15–17 . ANN is a tool that can be used to predict the fresh concrete properties as well as strength behavior, thus earlier studies proved ANN as an effective tool 18–23 . In this study, copper slag is incorporated in SCC at various proportions, and the experimental values for both fresh and hardened concrete are collected as dataset.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation and prediction of copper slag incorporated self‐compacting concrete using artificial neural network

Dinesh¹,

Dharmar²

2021

Structural Concrete

View full text Add to dashboard Cite

This research work is aimed to carryout experimental investigation on copper slag incorporated self-compacting (SCC) concrete; here, copper slag is used as a replacement to fine aggregate in the range of 0-100%. The self-compacting concrete is manufactured with powder matrix incorporating cement, flyash, metakaolin, and silicafume. The powder matrix is decided based on the objective of flow properties and strength properties. The fresh concrete properties of various mixes were studied and collected. Then, the concrete is casted as cubes and cylinders and tested for its strength behavior. The flow properties of copper slag-replaced mix were within stipulated guideline values by EFNARC, and early age strength attainment gets affected by the replacement of copper slag. The collected experimental results were designed to a data set categorizing as input and target. This data set is then used as a parameter in feed forward artificial neural network (ANN) and a predictive model is developed. This predictive model is then compared with the existing experimental values and tested for its performance. The results show that ANN provides a reliable predictive model for both flow and strength properties.

show abstract

“…Regarding the suitable accuracy indices, i.e., the coefficient of determination (R 2 ) and MAPE of 0.9831 and 0.1105, the Ada boost was introduced as a capable approximator. More studies concerning machine learning applications can be found in Ref.s [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

Moayedi¹,

Mosavi²

2021

Preprint

View full text Add to dashboard Cite

Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates artificial neural network (ANN) with a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

show abstract

Estimation of compressive strength of stirrup‐confined circular columns using artificial neural networks

Cited by 22 publications

References 24 publications

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Experimental investigation and prediction of copper slag incorporated self‐compacting concrete using artificial neural network

Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

Contact Info

Product

Resources

About