Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

Ahmad, Mahmood; Ahmad, Feezan; Huang, Jiandong; Iqbal, Muhammad Junaid; Safdar, Muhammad

doi:10.3934/mbe.2021454

Cited by 8 publications

(8 citation statements)

References 45 publications

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“…A recently developed approach based on data mining techniques has been increasingly employed to resolve real-world problems for the past half-decade, particularly in the field of civil engineering [18][19][20][21][22][23][24][25][26][27][28]. Several practical problems have already been effectively performed using machine learning algorithms, paving the way for new prospects in the construction industry.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Amjad

Ahmad

et al. 2022

Applied Sciences

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The major criteria that control pile foundation design is pile bearing capacity (Pu). The load bearing capacity of piles is affected by the various characteristics of soils and the involvement of multiple parameters related to both soil and foundation. In this study, a new model for predicting bearing capacity is developed using an extreme gradient boosting (XGBoost) algorithm. A total of 200 driven piles static load test-based case histories were used to construct and verify the model. The developed XGBoost model results were compared to a number of commonly used algorithms—Adaptive Boosting (AdaBoost), Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) using various performance measure metrics such as coefficient of determination, mean absolute error, root mean square error, mean absolute relative error, Nash–Sutcliffe model efficiency coefficient and relative strength ratio. Furthermore, sensitivity analysis was performed to determine the effect of input parameters on Pu. The results show that all of the developed models were capable of making accurate predictions however the XGBoost algorithm surpasses others, followed by AdaBoost, RF, DT, and SVM. The sensitivity analysis result shows that the SPT blow count along the pile shaft has the greatest effect on the Pu.

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Section: Introductionmentioning

confidence: 99%

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Amjad

Ahmad

et al. 2022

Applied Sciences

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“…The large amount of carbon dioxide emitted in the process of cement production is a serious burden and threat to the environment [ 5 , 6 , 7 ]. Finding new materials to replace some of the cement used in concrete is a necessary way to ensure the sustainable development of the concrete industry [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. At present, the use of fly ash, blast furnace slag, metakaolin, and other mineral admixtures to replace part of the cement used in concrete is the main solution to alleviate the large resource consumption and negative impact on the environment in the cement production process [ 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Zhu

Zhou

et al. 2022

Materials

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Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

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“…Cheng Yeh used a neural network model to simulate the slump flow of concrete [ 51 ]. The above machine methods achieved good results in the performance prediction of cement-based materials, and machine learning methods were widely used in the prediction of cement-based materials [ 33 , 34 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ]. Machine learning technology has been widely used in the cement-based materials performance evaluation process, but these methods still have some limitations, such as uncertainty, time-consuming, and low efficiency [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

et al. 2022

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Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.

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Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

Cited by 8 publications

References 45 publications

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

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