Data-driven prediction of the shear capacity of ETS-FRP-strengthened beams in the hybrid 2PKT–ML approach DOI Creative Commons
Thai Son Tran,

Boonchai Stitmannaithum,

Linh Van Hong Bui

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Nov. 14, 2023

Abstract A new approach that combines analytical two-parameter kinematic theory (2PKT) with machine learning (ML) models for estimating the shear capacity of embedded through-section (ETS)-strengthened reinforced concrete (RC) beams is proposed. The 2PKT was first developed to validate its representativeness and confidence against available experimental data ETS-retrofitted RC beams. Given deficiency test data, utilized generate a large pool 2643 samples. aim optimize ML algorithms, namely, random forest, extreme gradient boosting (XGBoost), light machine, artificial neural network (ANN) algorithm. optimized ANN model exhibited highest accuracy in predicting total strength ETS-strengthened ETS contribution. In terms beams, achieved R 2 values 0.99, 0.98, 0.96 training, validation, testing respectively. By contrast, could predict contribution high accuracy, 0.97 Then, effects all design variables on were investigated using hybrid 2PKT–ML. obtained trends well appraise reasonability proposed approach.

Language: Английский

Predictive models in machine learning for strength and life cycle assessment of concrete structures DOI

A. Dinesh,

B. Rahul Prasad

Automation in Construction, Journal Year: 2024, Volume and Issue: 162, P. 105412 - 105412

Published: April 3, 2024

Language: Английский

Citations

16
Machine Learning Approach for Prediction and Reliability Analysis of Failure Strength of U-Shaped Concrete Samples Joined with UHPC and PUC Composites DOI Open Access
Sadi Ibrahim Haruna, Yasser E. Ibrahim, Ibrahim Khalil Umar

et al.

Journal of Composites Science, Journal Year: 2025, Volume and Issue: 9(1), P. 23 - 23

Published: Jan. 6, 2025

To meet the increasing demand for resilient infrastructure in seismic and high-impact areas, accurate prediction reliability analysis of performance composite structures under impact loads is essential. Conventional techniques, including experimental testing high-quality finite element simulation, require considerable time resources. address these issues, this study investigated individual hybrid models support vector regression (SVR), Gaussian process (GPR), improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) predicting failure strength concrete developed by combining normal (NC) with ultra-high (UHPC) polyurethane-based polymer (PUC), considering different surface treatments subjected to various static loads. An dataset was utilized train ML perform on dataset. Key parameters included compressive (Cfc), flexural load U-shaped specimens (P), density (ρ), first crack (N1), splitting tensile (ft). Results revealed that all had high accuracy, achieving NSE values above acceptable thresholds greater than 90% across datasets. Statistical errors such as RMSE, MAE, PBIAS were calculated fall within limits. Hybrid IEPANN appeared be most effective model, demonstrating highest value 0.999 lowest PBIAS, MAE 0.0013, 0.0018, 0.001, respectively. The times (N1 N2) reduced survival probability increased.

Language: Английский

Citations

2
Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model DOI
Congcong Fan,

Yuanxun Zheng,

Shaoqiang Wang

et al.

Construction and Building Materials, Journal Year: 2023, Volume and Issue: 400, P. 132602 - 132602

Published: July 30, 2023

Language: Английский

Citations

26
Data-driven prediction on critical mechanical properties of engineered cementitious composites based on machine learning DOI Creative Commons

Shuangquan Qing,

Chuanxi Li

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: July 3, 2024

Abstract The present study introduces a novel approach utilizing machine learning techniques to predict the crucial mechanical properties of engineered cementitious composites (ECCs), spanning from typical exceptionally high strength levels. These properties, including compressive strength, flexural tensile and strain capacity, can not only be predicted but also precisely estimated. investigation encompassed meticulous compilation examination 1532 datasets sourced pertinent research. Four algorithms, linear regression (LR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGB), were used establish prediction model ECC determine optimal model. was utilized employ SHapley Additive exPlanations (SHAP) for scrutinizing feature importance conducting an in-depth parametric analysis. Subsequently, comprehensive control strategy devised properties. This provide actionable guidance design, equipping engineers professionals in civil engineering material science make informed decisions throughout their design endeavors. results show that RF demonstrated highest accuracy with R 2 values 0.92 0.91 on test set. XGB outperformed predicting 0.87 0.80 set, respectively. capacity least accurate. Meanwhile, MAE mere 0.84%, smaller than variability (1.77%) previous Compressive sensitivity variations both water-cement ratio (W) water reducer (WR). In contrast, exhibited solely changes W. Conversely, input features moderate consistent. attributes emerged combined effects multiple positive negative features. Notably, WR exerted most significant influence among all features, whereas polyethylene (PE) fiber as primary driver affecting capacity.

Language: Английский

Citations

13
Ensemble-learning model based ultimate moment prediction of reinforced concrete members strengthened by UHPC DOI
Woubishet Zewdu Taffese,

Yanping Zhu,

Genda Chen

et al.

Engineering Structures, Journal Year: 2024, Volume and Issue: 305, P. 117705 - 117705

Published: Feb. 29, 2024

Language: Английский

Citations

11
Advancing ultimate bond stress–slip model of UHPC structures through a novel hybrid machine learning approach DOI Creative Commons
Ahad Amini Pishro, Shiquan Zhang,

Qixiao Hu

et al.

Structures, Journal Year: 2024, Volume and Issue: 62, P. 106162 - 106162

Published: March 18, 2024

The significance of Ultimate Bond Stress-Slip (UBS-S) in reinforced Ultra-High Performance Concrete (UHPC) structures cannot be overstated, as it directly affects their load-carrying capacity, structural integrity, and long-term performance. A comprehensive analysis the UHPC-Parallel Micro Element System (UHPC-PMES), including 144 specimens, evaluated computational efficiency proposed UBS-S model. To this end, most critical settings steel bar diameter, concrete cover, bond length, UHPC compressive strength (db,c,lb,fUHPC′) were directed to create parametric research. Applying hybrid approach three different optimization techniques, namely Physics-Informed Neural Networks (PINN), Genetic Algorithms (GA), Multiple Linear Regression (MLR), study predicted at interface bars. It formulated hyper-parameters effect values (a,m,β,G). presented research used these algorithms solve an inverse problem engineering. Comparing results obtained from PINN, GA, MLR demonstrated that machine learning techniques PMES model could effectively accurately investigate ultimate stress-slip for structures.

Language: Английский

Citations

9
Research on Machine Learning with Algorithms and Development DOI Creative Commons
Liqiang Yu, Xingyu Zhao, Jiaxin Huang

et al.

Journal of Theory and Practice of Engineering Science, Journal Year: 2023, Volume and Issue: 3(12), P. 7 - 14

Published: Dec. 29, 2023

Machine Learning, as one of the key technologies in field artificial intelligence, has made significant advancements recent years. This study provides a relatively systematic introduction to machine learning. Firstly, it gives an overview historical development learning, and then focuses on analysis classical algorithms Subsequently, elucidates latest research aiming comprehensively explore applications learning various domains discuss potential future directions.

Language: Английский

Citations

21
Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms DOI Creative Commons
Yanhua Yang, Guiyong Liu, Haihong Zhang

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(1), P. 190 - 190

Published: Jan. 11, 2024

Machine learning (ML) algorithms have been widely used in big data prediction and analysis terms of their excellent regression ability. However, the accuracy different ML varies between problems sets. In order to construct a model with optimal for fly ash concrete (FAC), such as genetic programming (GP), support vector (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) adaptive network-based fuzzy inference system (ANFIS) were selected this study; particle swarm optimization (PSO) algorithm was also optimize structure hyperparameters each algorithm. The statistical results show that performance assembled is better than an NN-based addition, PSO can effectively improve algorithms. comprehensive analyzed using Taylor diagram, PSO-XGBoost has best performance, R2 MSE equal 0.9072 11.4546, respectively.

Language: Английский

Citations

8
Bond strength between recycled aggregate concrete and rebar: Interpretable machine learning modeling approach for performance estimation and engineering design DOI
Li Li,

Yihang Guo,

Yang Zhang

et al.

Materials Today Communications, Journal Year: 2024, Volume and Issue: 39, P. 108945 - 108945

Published: April 16, 2024

Language: Английский

Citations

6
Intelligent predicting and monitoring of ultra-high-performance fiber reinforced concrete composites − A review DOI
Dingqiang Fan,

Ziao Chen,

Yuan Cao

et al.

Composites Part A Applied Science and Manufacturing, Journal Year: 2024, Volume and Issue: unknown, P. 108555 - 108555

Published: Oct. 1, 2024

Language: Английский

Citations

5