Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Li, Qingfu; Song, Zong-Ming

doi:10.3390/app12115442

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Ensemble learning can be widely used in defect analysis and remaining useful life prediction in engineering. Li and Song 133 build six models based ensemble learning to predict bridge deck defect, and the learning model based on XGBoost is found optimal, whose explanatory agrees with the fact. Li et al 134 propose an ensemble learningbased prognostic approach that considers the influence of time-dependent degradation to assess the remaining useful life.…”

Section: Ensemble Learningsupporting

confidence: 54%

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

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Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.

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Section: Ensemble Learningsupporting

confidence: 54%

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

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show abstract

“…The study employs the Adaptive Synthetic (ADASYN) sampling technique to handle imbalanced data, enhancing the prediction quality. Li et al [35] aimed to assist bridge managers in making informed maintenance decisions for steel bridge deck defects by developing an ensemble-learning-based prediction model. The research employed ADASYN to address data imbalance issues in the 2021 NBI database and built six ensemble learning models with optimized hyperparameters through grid search.…”

Section: Adasyn Data Balancingmentioning

confidence: 99%

“…Yet, machine learning algorithms frequently encounter challenges such as outliers and imbalanced datasets, which can reduce accuracy. Research has demonstrated that addressing these issues by applying the Isolation Forest (iForest) method to remove outliers [23][24][25][26][27] and utilizing Adaptive Synthetic Sampling (ADASYN) for balancing imbalanced data [28][29][30][31][32][33][34][35] can lead to improved predictive system performance.…”

Section: Introductionmentioning

confidence: 99%

Customer Shopping Behavior Analysis Using RFID and Machine Learning Models

Alfian,

Octava,

Hilmy

et al. 2023

Information

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Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted from RSS data and machine learning models were utilized to classify different customer shopping activities. We proposed integration of iForest Outlier Detection, ADASYN data balancing and Multilayer Perceptron (MLP). The results indicate that the proposed model performed better than other supervised learning models, with improvements of up to 97.778% in accuracy, 98.008% in precision, 98.333% in specificity, 98.333% in recall, and 97.750% in the f1-score. Finally, we showcased the integration of this trained model into a web-based application. This result can assist managers in understanding customer preferences and aid in product placement, promotions, and customer recommendations.

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“…Datadriven artificial intelligence techniques can break through artificial empirical perception and eliminate dependence on humans [12,13]. Integrated learning methods represented by XGBoost models have become increasingly popular among researchers in engineering prediction problems in recent years [14,15]. Lyngdoh et al used several popular machine learning models for concrete strength prediction.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost

Wei,

Ji,

et al. 2023

Applied Sciences

Self Cite

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Steel bridges are widely used in bridge engineering. In the structural design of steel bridge deck pavement systems, engineers focus on obtaining mechanical properties by calculating design parameters and are keen to establish a quick and accurate solution method. Because of the complex knowledge system involved in the numerical calculation method, it is difficult for the general engineering designer to master it. Researchers have started using artificial intelligence algorithms to solve problems in civil engineering. This study developed an XGBoost-based mechanical performance prediction model for steel bridge deck pavement systems. First, numerical simulation tests are conducted at unfavorable load locations using a finite element model to establish a dataset. Then, an XGBoost model is built using this dataset, and its parameters are optimized and compared with traditional machine learning models. Finally, an explanatory analysis of the model is performed using SHAP, an interpretable machine learning framework. The results indicate that the developed XGBoost model accurately predicts the mechanical properties of steel bridge deck pavement systems.

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Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Cited by 11 publications

References 33 publications

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Customer Shopping Behavior Analysis Using RFID and Machine Learning Models

Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost

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