A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

Yong, Weixun; Zhou, Jian; Armaghani, Danial Jahed; Tahir, Mahmood Md.; Tarinejad, Reza; Pham, Binh Thai; Khang, Huynh Van

doi:10.1007/s00366-019-00932-9

Cited by 68 publications

(20 citation statements)

References 95 publications

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“…e investigations explored that the ANFIS model yielded superior accuracy compared with other machine learning techniques and experimental data points. However, the ANFIS model suffers from a number of limitations; for instance, it is weak in finding the optimal firing strength [86,87]. By using several metaheuristic optimization techniques such as genetic algorithm or simulated annealing as examples in [88,89], it is possible to search for and better determine the firing strengths of parameters.…”

Section: Machine Learning Method: Adaptive Neurofuzzy Inferencementioning

confidence: 99%

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Phan

2020

Advances in Civil Engineering

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The ultimate compressive load of concrete-filled steel tubular (CFST) structural members is recognized as one of the most important engineering parameters for the design of such composite structures. Therefore, this paper deals with the prediction of ultimate load of rectangular CFST structural members using the adaptive neurofuzzy inference system (ANFIS) surrogate model. To this end, compression test data on CFST members were extracted from the available literature, including: (i) the mechanical properties of the constituent materials (i.e., steel’s yield strength and concrete’s compressive strength) and (ii) the geometric parameters (i.e., column length, width and height of cross section, and steel tube thickness). The ultimate load is the output response of the problem. The ANFIS model was trained using a hybrid of the least-squares and backpropagation gradient descent method. Quality assessment criteria such as coefficient of determination (R2), root mean square error (RMSE), and slope of linear regression were used for error measurements. A 11-fold cross-validation technique was employed to evaluate the performance of the model. Results showed that for the training process, the average performance was as follows: R2, RMSE, and slope were 0.9861, 89.83 kN, and 0.9861, respectively. For the validating process, the average performance was as follows: R2, RMSE, and slope were 0.9637, 140.242 kN, and 0.9806, respectively. Therefore, the ANFIS model may be considered valid because it performs well in predicting ultimate load using the validated data. Moreover, partial dependence (PD) analysis was employed to interpret the “black-box” ANFIS model. It is observed that PD enabled us to locally track the influence of each input variable on the output response. Besides reliable prediction of ultimate load, ANFIS can also provide maps of ultimate load. Finally, the ANFIS model developed in this study was compared with other works in the literature, showing that the ANFIS model could improve the accuracy of ultimate load prediction, in comparison to previously published results.

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Section: Machine Learning Method: Adaptive Neurofuzzy Inferencementioning

confidence: 99%

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Phan

2020

Advances in Civil Engineering

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“…For the training dataset, the RF obtained the highest rank (25) while the SVM obtained the lowest rank (5). For the testing dataset, the ANN achieved the highest rank (22) while the CHAID achieved the lowest rank (6). Turning to the performance indices, the RF outperformed other models developed for the training dataset.…”

Section: Evaluation Of the Developed Modelsmentioning

confidence: 93%

Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

et al. 2020

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Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.

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“…It is worth mentioning that if the model is a perfect fit for the data, then the R is 1, and MSE and MAE are 0. Thus, the R-value of much less than one and a higher MSE and MAE indicate a poorer prediction (Yong et al, 2020).…”

Section: Performance Indicatorsmentioning

confidence: 99%

Mechanical properties modeling of severely plastically deformed biodegradable ZK60 magnesium alloy for bone implants

Zhang

Wang

et al. 2020

Lat. Am. j. solids struct.

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The effectiveness of magnesium (Mg) alloys to improve the capability of bone tissue generation may be severely diminished if the required mechanical properties are not provided. Here, the effort is directed to model the mechanical performance of severely plastically deformed biodegradable ZK60 Mg alloy in bone regeneration protocols. For this purpose, the effects of parallel tubular channel angular pressing (PTCAP) on yield strength (σYS), ultimate tensile strength (σUTS), and elongation to failure (δ) were addressed. Given the multifaceted variables of the PTCAP with nonlinear interactions, a precise determination of the mechanical properties requires a large number of experiments. Therefore, gene expression programming (GEP) and genetic programming (GP) models were proposed to achieve appropriate combinations of mechanical properties for bone implant purposes based on a rational hypothesis that for correlation coefficient (|R|) higher than 0.8, a strong correlation is established between the predicted and measured values. The results verified that the highest mechanical performance was achieved at the second pass of PTCAP, thus has a great potential to be the most promising candidate for biodegradable implant material. Besides, the proposed models were capable of precisely predicting the mechanical performance of the SPD-processed biodegradable ZK60 Mg.

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A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles

Cited by 68 publications

References 95 publications

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

Mechanical properties modeling of severely plastically deformed biodegradable ZK60 magnesium alloy for bone implants

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