Application of supervised learning for classification of cracking and non-cracking major damage in TRMs based on AE features DOI Creative Commons

Khan Junaid,

Amir Si Larbi,

N. Algourdin

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 437, P. 137079 - 137079

Published: June 14, 2024

Language: Английский

Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and GEP models DOI Creative Commons
Mostafa Mohammadzadeh Taleshi, Nima Tajik,

Alireza Mahmoudian

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e02842 - e02842

Published: Jan. 4, 2024

This study employs soft computing techniques, including artificial neural network (ANN) models and gene expression programming (GEP), to enhance the prediction of ultimate load in timber pull-out tests under varying environmental conditions. A comprehensive dataset 202 samples normal conditions 324 harsh was gathered. Distinct were developed for each scenario, achieving commendable accuracies. The ANN performed at 0.91 0.99 conditions, while GEP 0.94, respectively. also predicted free end slip using an model with accuracy 0.97. SHapley values technique employed assess impact features on models, revealing specific influential features. In rod type most influential, bonded length demonstrated highest impact. Additionally, duration immersion feature had substantial effect predicting slip. final section compared data experimental values, showing a noteworthy correlation over 60% between outputs established models.

Language: Английский

Citations

20
An explainable artificial-intelligence-aided safety factor prediction of road embankments DOI Creative Commons
Azam Abdollahi, Deli Li, Jian Deng

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108854 - 108854

Published: July 4, 2024

Despite the widespread application of data-centric techniques in Geotechnical Engineering, there is a rising need for building trust artificial intelligence (AI)-driven safety assessment road embankments due to its so-called "black-box" nature. In addition, from lens limit equilibrium approaches, e.g., Bishop, Fellenius, Janbu and Morgenstern–Price, finite element method, it essential carefully examine interplay both topological physical/mechanical properties during factor (FoS) predictions. First, aside having conventional geotechnical inputs soil core foundation height embankments, this paper codifies geometric features innovatively. The number slope types with different ratios including 1:1, 1.5:1 2:1 as well berms introduced. Second, pool 19 machine learning (ML) effortlessly trained on dataset using an automated ML (AutoML) pipeline identify most optimized algorithm. Finally, achieve post-hoc interpretability internal mechanism input–output relationship unbiasedly, game-theory-based explainable AI (XAI) method called Shapley additive explanations (SHAP) values applied. SHAP-aided importance analysis provides human-interpretable insights indicates height, California bearing ratio, type cohesion influential parameters. Exclusively, analyzing hazardous by classifying main joint contributors exhibits complex highly variable influence FoS. This harnesses power XAI tools enhance reliability transparency rapid FoS prediction slopes. It targets researchers, practitioners, decision-makers, general public first time.

Language: Английский

Citations

18
A comprehensive study on the mechanical properties of natural fiber reinforced stabilized rammed earth using experimental and data-driven fuzzy logic-based analysis DOI Creative Commons

Aryan Baibordy,

Mohammad Yekrangnia, Saeed Ghaffarpour Jahromi

et al.

Cleaner Materials, Journal Year: 2025, Volume and Issue: unknown, P. 100300 - 100300

Published: Feb. 1, 2025

Language: Английский

Citations

2
Advancing forest fire prediction: A multi-layer stacking ensemble model approach DOI

Fahad Shahzad,

Kaleem Mehmood, Shoaib Ahmad Anees

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 19, 2025

Language: Английский

Citations

2
Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review DOI Creative Commons
Harikesh Singh, Li-Minn Ang, Tom Lewis

et al.

Journal of Forestry Research, Journal Year: 2024, Volume and Issue: 35(1)

Published: Sept. 27, 2024

Language: Английский

Citations

13
Application of Data-Driven Surrogate Models in Structural Engineering: A Literature Review DOI
Delbaz Samadian, Imrose B. Muhit, Nashwan Dawood

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 13, 2024

Language: Английский

Citations

12
A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs DOI Creative Commons

Alireza Mahmoudian,

Mussa Mahmoudi, Mohammad Yekrangnia

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: Feb. 27, 2025

Language: Английский

Citations

1
Critical temperature prediction in cold-formed steel columns exposed to local fire DOI
Ravikant Singh, Avik Samanta

Journal of Constructional Steel Research, Journal Year: 2025, Volume and Issue: 229, P. 109509 - 109509

Published: March 11, 2025

Language: Английский

Citations

1
Beyond Development: Challenges in Deploying Machine-Learning Models for Structural Engineering Applications DOI
Mohsen Zaker Esteghamati, Brennan Bean, Henry Burton

et al.

Journal of Structural Engineering, Journal Year: 2025, Volume and Issue: 151(6)

Published: March 30, 2025

Language: Английский

Citations

1
Machine learning-based Shapley additive explanations approach for corroded pipeline failure mode identification DOI
Mohamed El Amine Ben Seghier, Osama Ahmed Mohamed,

Hocine Ouaer

et al.

Structures, Journal Year: 2024, Volume and Issue: 65, P. 106653 - 106653

Published: June 14, 2024

Language: Английский

Citations

6