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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:

Interpretable machine-learning models for maximum displacements of RC beams under impact loading predictions
Dade Lai, Cristoforo Demartino, Yan Xiao
Engineering Structures (2023) Vol. 281, pp. 115723-115723
Closed Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach
Celal Çakıroğlu, Yaren Aydın, Gebrai̇l Bekdaş, et al.
Materials (2023) Vol. 16, Iss. 13, pp. 4578-4578
Open Access | Times Cited: 40
Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups
Faruk Ergen, Metin Katlav
Journal of Building Engineering (2024) Vol. 83, pp. 108455-108455
Closed Access | Times Cited: 27
Bond stress estimation of profiled steel-concrete in steel reinforced concrete composite structures using ensemble machine learning approaches
Shuyuan Zhang, Jinjun Xu, Tao Lai, et al.
Engineering Structures (2023) Vol. 294, pp. 116725-116725
Closed Access | Times Cited: 22
Investigating the applicability of deep learning and machine learning models in predicting the structural performance of V-shaped RC folded plates
Metin Katlav, Faruk Ergen, Kâzım Türk, et al.
Materials Today Communications (2024) Vol. 38, pp. 108141-108141
Closed Access | Times Cited: 10
Probabilistic models of dynamic increase factor (DIF) for reinforced concrete structures: A Bayesian approach
Dade Lai, Fabrizio Nocera, Cristoforo Demartino, et al.
Structural Safety (2024) Vol. 108, pp. 102430-102430
Closed Access | Times Cited: 9
Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber
Faruk Ergen, Metin Katlav
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 6, pp. 4541-4562
Closed Access | Times Cited: 7
Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading
Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, et al.
Results in Engineering (2024), pp. 103135-103135
Open Access | Times Cited: 7
Machine learning driven post-impact damage state prediction for performance-based crashworthiness design of bridge piers
Chang Zhou, Yazhou Xie, Wenwei Wang, et al.
Engineering Structures (2023) Vol. 292, pp. 116539-116539
Closed Access | Times Cited: 20
Data-driven moment-carrying capacity prediction of hybrid beams consisting of UHPC-NSC using machine learning-based models
Metin Katlav, Faruk Ergen
Structures (2023) Vol. 59, pp. 105733-105733
Closed Access | Times Cited: 18
Probabilistic machine leaning models for predicting the maximum displacements of concrete-filled steel tubular columns subjected to lateral impact loading
Dade Lai, Cristoforo Demartino, Yan Xiao
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108704-108704
Closed Access | Times Cited: 5
Machine learning predictions for bending capacity of ECC-concrete composite beams hybrid reinforced with steel and FRP bars
Wenjie Ge, Feng Zhang, Yi Wang, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03670-e03670
Open Access | Times Cited: 5
Explainable machine learning driven strength degradation investigation of BFRP bar in seawater and sea sand concrete environment
Wutong Zhang, Wenwei Wang, Yixing Tang, et al.
Structures (2025) Vol. 71, pp. 108205-108205
Closed Access
A backtracking search-based extreme gradient boosting algorithm for soil moisture prediction using meteorological variables
Hojjat Emami, Somayeh Emami, Vahid Rezaverdinejad
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access
Exploring the stainless-steel beam-to-column connections response: A hybrid explainable machine learning framework for characterization
Sina Sarfarazi, Rabee Shamass, Federico Guarracino, et al.
Frontiers of Structural and Civil Engineering (2025)
Closed Access
A Machine learning-based model to predict residual stress in aluminum shell formed by shot peening
Amirhossein Golmohammadi, Hossein Soroush, S. Khodaygan
International Journal of Solids and Structures (2025), pp. 113250-113250
Closed Access
Data-driven modeling of the quantitative structure-activity relationship between aggregate contact parameters and dynamic modulus in asphalt mixtures
Lin Kong, Xiuquan Lin, Pengfei Wu, et al.
Construction and Building Materials (2025) Vol. 470, pp. 140698-140698
Closed Access
Fatigue Predictive Modeling of Composite Materials for Wind Turbine Blades Using Explainable Gradient Boosting Models
Yaren Aydın, Celal Çakıroğlu, Gebrai̇l Bekdaş, et al.
Coatings (2025) Vol. 15, Iss. 3, pp. 325-325
Open Access
Behavior of Lightweight Wood‐Frame Shear Walls with Plybamboo and Wood Sheathing Panels: Experimental Study, Numerical Simulation, and Parametric Models for the Design Process
Renjie Wu, Yongjia Xu, Yubing Hou, et al.
Earthquake Engineering & Structural Dynamics (2025)
Closed Access
Maximum displacement prediction model for steel beams with hexagonal web openings under impact loading based on artificial neural networks
Huayan Chen, Yongbin Liu, Yating Huang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108932-108932
Closed Access | Times Cited: 4
Mechanism analysis for scaling effect on the impact behaviors of RC beam: from material properties to component response
Jian Li, Renbo Zhang, Liu Jin, et al.
International Journal of Impact Engineering (2024) Vol. 195, pp. 105105-105105
Closed Access | Times Cited: 3
AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering
Metin Katlav, Mehmet Emin Tabar, Kâzım Türk
Materials Today Communications (2024) Vol. 42, pp. 111286-111286
Closed Access | Times Cited: 3
Risk assessment and prevention for typical railway bridge pier under rockfall impact
Zhiyang Cao, Zhanhui Liu, Guoji Xu, et al.
Structures (2024) Vol. 62, pp. 106178-106178
Closed Access | Times Cited: 2
Deep learning-based prediction of structural responses of RC slabs subjected to blast loading
Xiaoqing Zhou, Bing-Gui Huang, Xiaoyou Wang, et al.
Engineering Structures (2024) Vol. 311, pp. 118184-118184
Closed Access | Times Cited: 2
Experimental investigation of dynamic response of full-scale RC beams under high-energy impact
Wang Yun, Peng Yu, Y.C. Wang, et al.
International Journal of Impact Engineering (2024) Vol. 195, pp. 105104-105104
Closed Access | Times Cited: 2
Neighborhood component analysis-based feature selection in machine learning to predict tendon ultimate stress of unbonded prestressed concrete beams
Zhaodong Ding, Hexiang Liu, Cristoforo Demartino, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03428-e03428
Open Access | Times Cited: 1
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