Explainable machine learning based efficient prediction tool for lateral cyclic response of post-tensioned base rocking steel bridge piers

Wakjira, Tadesse G.; Rahmzadeh, Ahmad; Alam, M. Shahria; Tremblay, Robert

doi:10.1016/j.istruc.2022.08.023

Cited by 33 publications

(21 citation statements)

References 33 publications

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“…These models are categorized as standalone and ensemble models. Standalone models used in the study include LR, SVM, ANN, and KNN, and ensemble models include bagging, GBM, XGBoost, RF, and ET [34,37,49,56,57].…”

Section: Modeling and Analysismentioning

confidence: 99%

“…Machine learning (ML) or artificial intelligence (AI) techniques, however, use implicit algorithms to capture both linear and nonlinear relationships compared to empirical regressions [14,[34][35][36] to solve intricate problems [37], and can therefore be applied for effective estimation of the TDS from EC and temperature. AI models have been strongly recommended in recent years for the prediction of WQPs and have been applied in the accurate estimation of water quality index (WQI) compared to conventional models [38,39].…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble predictive models are the aggregation of predictions from several ML models to create an overall stronger prediction [40][41][42]. Examples of standalone include support vector machines (SVM), artificial neuron networks (ANN), linear regressors (LR), and K-nearest neighbor model (KNN), while ensemble methods include bagging, gradient boosting machines (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) [37,[41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

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Section: Modeling and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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“…The performances of different machine learning algorithms, such as k-nearest neighbors, naïve Bayes, and random forest, were compared. Wakjira et al (2022) investigated the performance of machine learning techniques in predicting the lateral cyclic response of post-tensioned base rocking steel bridge piers and proposed an explainable machine learning based predictive model. Malekjafarian et al (2019) proposed a two-stage machine learning approach to detect damage in bridges using the responses obtained from a passing vehicle.…”

Section: Introductionmentioning

confidence: 99%

Application of neural networks to the prediction of the compressive capacity of corroded steel plates

Zhang

Vaccaro²,

Zaghi³

2023

Front. Built Environ.

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The application of artificial neural network approaches has been successful in solving complex civil engineering problems, such as damage detection and structural member capacity prediction. Within the context of the present study, corrosion has become the main factor limiting the safety and load-carrying capacity of aging steel bridge girders. Corrosion damage is often most severe near girder ends in simple-span bridges due to deck joint leakage and the pooling of water and de-icing salts. In addition to empirical methods, Finite Element (FE) analysis is typically used to evaluate the residual bearing capacity of corroded steel girders. However, it is prohibitively challenging and time-consuming to create an accurate FE model of a corroded girder due to the irregular nature of corrosion damage. Resultantly, corrosion damage is often reduced to uniform section loss, which leads to unreliable estimates of a girder’s residual bearing capacity. Researchers have proposed methodologies for modeling irregular corrosion damage, but these approaches require a high level of expertise. A comprehensive method is therefore required to efficiently estimate the residual bearing capacity of a corroded steel girder. This paper proposes the use of neural networks to predict the residual bearing capacity of corroded steel plate models as a first step in estimating the residual bearing capacity of an in-service girder. Neural networks are constructed and trained on a database built from FE analysis performed on steel plate models with realistic representations of corrosion damage. This study assesses the ability of neural networks to estimate the compressive capacity of corroded steel plates since plate girders are one of the most prevalent girder forms in steel bridges. Three types of neural networks are trained to predict the compressive capacity of corroded plate models, including a multilayer perceptron (MLP), a convolutional neural network (CNN), and a hybrid MLP-CNN model. The average mean absolute percentage errors (MAPE) for the three models are 20.65%, 11.46%, and 9.64%, respectively. The results of this study demonstrate the potential of using neural networks to predict the compressive capacity of corroded plates efficiently and accurately, which could facilitate proactive maintenance decision-making for aging bridges.

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“…In addition, recent studies have developed a flexural capacity prediction model as well as an efficient and user-friendly software tool for both fiber-reinforced polymer (FRP)-RC beams [15] and corroded RC beams [16]. The process of establishing the above model is achieved through training and evaluating various ML models, ranging from the simplest to the most complex, to determine the most efficient and accurate model [17]. However, the above model cannot achieve multioutput prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

et al. 2023

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To obtain the magnesium oxychloride cement concrete (MOCC) ratio with excellent water resistance quickly and accurately, a BP neural network (BPNN) model with a topology structure of 4-10-2 was designed, and the PSO (particle swarm optimization), GWO (gray wolf optimization), and WOA (whale optimization algorithm) algorithms were used to optimize the model. The input layer parameters of the model above were n(MgO/MgCl2), Grade I fly ash, phosphoric acid (PA), and phosphate fertilizer (PF) content, and the output layer was the MOCC’s compressive strength and softening coefficient. The model had a dataset of 144 groups, including 100 training set data, 22 verification set data, and 22 test set data. The results showed that the PSO-BPNN model had the highest predictive accuracy among the four models, with a mean R2 of 0.99, mean absolute error(MAE) of 0.52, mean absolute percentage error(MAPE) of 0.01, and root mean square error (RMSE) of 0.73 in predicting compressive strength, and a mean R2 of 0.99, MAE of 0.44, MAPE of 0.01, and RMSE of 0.62 in predicting the softening coefficient. The results showed that using the PSO-BPNN to predict the compressive strength and softening coefficient of MOCC is feasible and can provide theoretical guidance for designing the MOCC mix.

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Explainable machine learning based efficient prediction tool for lateral cyclic response of post-tensioned base rocking steel bridge piers

Cited by 33 publications

References 33 publications

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Application of neural networks to the prediction of the compressive capacity of corroded steel plates

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network

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