OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Explainable machine learning-aided efficient prediction model and software tool for bond strength of concrete with corroded reinforcement
Tadesse G. Wakjira, Abdelrahman Abushanab, M. Shahria Alam, et al.
Structures (2023) Vol. 59, pp. 105693-105693
Closed Access | Times Cited: 13
Tadesse G. Wakjira, Abdelrahman Abushanab, M. Shahria Alam, et al.
Structures (2023) Vol. 59, pp. 105693-105693
Closed Access | Times Cited: 13
Showing 13 citing articles:
Hybrid machine learning model and predictive equations for compressive stress-strain constitutive modelling of confined ultra-high-performance concrete (UHPC) with normal-strength steel and high-strength steel spirals
Tadesse G. Wakjira, Abdelrahman Abushanab, M. Shahria Alam
Engineering Structures (2024) Vol. 304, pp. 117633-117633
Open Access | Times Cited: 18
Tadesse G. Wakjira, Abdelrahman Abushanab, M. Shahria Alam
Engineering Structures (2024) Vol. 304, pp. 117633-117633
Open Access | Times Cited: 18
Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach
Celal Çakıroğlu, Yaren Aydın, Gebrai̇l Bekdaş, et al.
Energy and Buildings (2024) Vol. 313, pp. 114254-114254
Closed Access | Times Cited: 9
Celal Çakıroğlu, Yaren Aydın, Gebrai̇l Bekdaş, et al.
Energy and Buildings (2024) Vol. 313, pp. 114254-114254
Closed Access | Times Cited: 9
Machine learning for design, optimization and assessment of steel-concrete composite structures: A review
Xianlin Wang, Bozhou Zhuang, Danny Smyl, et al.
Engineering Structures (2025) Vol. 328, pp. 119652-119652
Closed Access
Xianlin Wang, Bozhou Zhuang, Danny Smyl, et al.
Engineering Structures (2025) Vol. 328, pp. 119652-119652
Closed Access
Modeling prediction of bond strength between rebar and recycled aggregate concrete by deep learning approach based on attention mechanism
Haifeng Yang, Haifeng Yang, Zengfeng Zhao
Construction and Building Materials (2025) Vol. 471, pp. 140753-140753
Closed Access
Haifeng Yang, Haifeng Yang, Zengfeng Zhao
Construction and Building Materials (2025) Vol. 471, pp. 140753-140753
Closed Access
Bond strength between recycled aggregate concrete and rebar: Interpretable machine learning modeling approach for performance estimation and engineering design
Li Li, Yihang Guo, Yang Zhang, et al.
Materials Today Communications (2024) Vol. 39, pp. 108945-108945
Closed Access | Times Cited: 4
Li Li, Yihang Guo, Yang Zhang, et al.
Materials Today Communications (2024) Vol. 39, pp. 108945-108945
Closed Access | Times Cited: 4
Explainable Ensemble Learning Graphical User Interface for Predicting Rebar Bond Strength and Failure Mode in Recycled Coarse Aggregate Concrete
Celal Çakıroğlu, Tanvir Hassan Tusher, Md. Shahjalal, et al.
Developments in the Built Environment (2024), pp. 100547-100547
Open Access | Times Cited: 2
Celal Çakıroğlu, Tanvir Hassan Tusher, Md. Shahjalal, et al.
Developments in the Built Environment (2024), pp. 100547-100547
Open Access | Times Cited: 2
Rapid shear capacity prediction of TRM-strengthened unreinforced masonry walls through interpretable machine learning deployed in a web app
Petros C. Lazaridis, Athanasia Κ. Thomoglou
Journal of Building Engineering (2024) Vol. 98, pp. 110912-110912
Closed Access | Times Cited: 2
Petros C. Lazaridis, Athanasia Κ. Thomoglou
Journal of Building Engineering (2024) Vol. 98, pp. 110912-110912
Closed Access | Times Cited: 2
An efficient deep learning approach for ultimate bond strength estimations of corroded bar and concrete
Lukesh Parida, Sumedha Moharana, Rima Sahani
Engineering Research Express (2024) Vol. 6, Iss. 4, pp. 045109-045109
Closed Access | Times Cited: 1
Lukesh Parida, Sumedha Moharana, Rima Sahani
Engineering Research Express (2024) Vol. 6, Iss. 4, pp. 045109-045109
Closed Access | Times Cited: 1
Multi-target machine learning-assisted design of sustainable steel fibre-reinforced concrete
Elyas Asadi Shamsabadi, Saeed Mohammadzadeh Chianeh, Peyman Zandifaez, et al.
Structures (2024) Vol. 71, pp. 108036-108036
Open Access | Times Cited: 1
Elyas Asadi Shamsabadi, Saeed Mohammadzadeh Chianeh, Peyman Zandifaez, et al.
Structures (2024) Vol. 71, pp. 108036-108036
Open Access | Times Cited: 1
Optimized Machine Learning Model for Predicting Compressive Strength of Alkali-Activated Concrete Through Multi-Faceted Comparative Analysis
Guohua Fang, Lin Zhu, C.Y. Xie, et al.
Materials (2024) Vol. 17, Iss. 20, pp. 5086-5086
Open Access
Guohua Fang, Lin Zhu, C.Y. Xie, et al.
Materials (2024) Vol. 17, Iss. 20, pp. 5086-5086
Open Access
Deep learning assisted prediction on main factors influencing shear strength of sintered nano Ag-Al joints under high temperature aging
Libo Zhao, Yanwei Dai, Fei Qin
Engineering Failure Analysis (2024) Vol. 167, pp. 109028-109028
Closed Access
Libo Zhao, Yanwei Dai, Fei Qin
Engineering Failure Analysis (2024) Vol. 167, pp. 109028-109028
Closed Access
Explainable artificial intelligence framework for plastic hinge length prediction of flexural-dominated steel-reinforced concrete composite shear walls
Chaochao Quan, Wang We, Kuahai Yu, et al.
Engineering Structures (2024) Vol. 324, pp. 119388-119388
Closed Access
Chaochao Quan, Wang We, Kuahai Yu, et al.
Engineering Structures (2024) Vol. 324, pp. 119388-119388
Closed Access
Flexural capacity prediction of reinforced UHPC beams using an interpretable machine learning model
Shiqiang Qin, Jun Li, Renxian Song, et al.
Structural Engineering International (2024), pp. 1-12
Closed Access
Shiqiang Qin, Jun Li, Renxian Song, et al.
Structural Engineering International (2024), pp. 1-12
Closed Access
