Prediction of shear capacity of channel shear connectors using the ANFIS model

Toghroli, Ali; Mohammadhassani, Mohammad; Suhatril, Meldi; Shariati, Mahdi; Ibrahim, Zainah

doi:10.12989/scs.2014.17.5.623

Cited by 175 publications

(32 citation statements)

References 20 publications

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“…Machine learning is a bionics-based problem solution scheme which mimics the functionality of human brain [1][2][3][4][5][6][7][8][9][10][11][12][13]. It is usually constituted by a series of mathematic operations and transformations which are implemented by computer programs coupled with modern electronic computers.…”

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning hydrological model for flood forecast purpose

Kan¹,

Liang²,

Yu³

et al. 2020

Open Geosciences

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Machine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems.

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Section: Introductionmentioning

confidence: 99%

Hybrid machine learning hydrological model for flood forecast purpose

Kan¹,

Liang²,

Yu³

et al. 2020

Open Geosciences

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“…The present study used five performance indices to assess the performance of the models developed. These included coefficient of determination (R 2 ), mean absolute error (MAE), root-mean-square error (RMSE), variance accounted for (VAF), and a20-index [32,[40][41][42]44,[94][95][96][97]. These indices were widely used in previous studies for the performance assessment of ML models.…”

Section: Assessment Of the Proposed Modelsmentioning

confidence: 99%

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

et al. 2020

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In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields.

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“…e neural network uses the backpropagation (BP) procedure. e backpropagation learning algorithm, designed to train a feed-forward network, is an effective learning technique used to exploit the regularities and exceptions in the training sample [36,37]. Backpropagation neural network (BPNN) which will be used in this research can be considered the most fundamental and widely used method among the ANN methods.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria‐Based Concrete

al-Swaidani

Khwies

2018

Advances in Civil Engineering

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Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

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Prediction of shear capacity of channel shear connectors using the ANFIS model

Cited by 175 publications

References 20 publications

Hybrid machine learning hydrological model for flood forecast purpose

Hybrid machine learning hydrological model for flood forecast purpose

A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration

Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria‐Based Concrete

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