Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

Optimization-driven XGBoost model with metaheuristic algorithms for assessing compressive strength of high-performance concrete

Amit Kumar Rai and Shiv Shankar Kumar
Asian Journal of Civil Engineering (2025)
https://doi.org/10.1007/s42107-025-01379-8

A novel tool for probabilistic modeling of liquefaction behavior in alluvial soil

Sufyan Ghani and Sunita Kumari
Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 19 (1) 134 (2025)
https://doi.org/10.1080/17499518.2024.2395560

Artificial bee colony optimized random forest model for prediction of fly ash concrete compressive strength

Manish Bali, Ved Prakash Mishra and Anuradha Yenkikar
MethodsX 14 103412 (2025)
https://doi.org/10.1016/j.mex.2025.103412

Predicting tunnel boring machine performance with the Informer model: a case study of the Guangzhou Metro Line project

Junxing Zhao and Xiaobin Ding
Journal of Zhejiang University-SCIENCE A 26 (3) 226 (2025)
https://doi.org/10.1631/jzus.A2400012

Interfacial bond capacity prediction of concrete-filled steel tubes utilizing artificial neural network

Hatem H. Almasaeid, Donia G. Salman, Raed M. Abendeh, Rabab A. Allouzi and Hesham S. Rabayah
Cogent Engineering 11 (1) (2024)
https://doi.org/10.1080/23311916.2023.2297501

Universal boosting ML approaches to predict the ultimate load capacity of CFST columns

Thuy‐Anh Nguyen, Khuong Le Nguyen and Hai‐Bang Ly
The Structural Design of Tall and Special Buildings 33 (2) (2024)
https://doi.org/10.1002/tal.2071

Hybrid Machine Learning Model Based on GWO and PSO Optimization for Prediction of Oilwell Cement Compressive Strength under Acidic Corrosion

Li Wang, Sheng Huang, Zaoyuan Li, Donghua Su, Yang Liu and Yue Shi
SPE Journal 29 (09) 4684 (2024)
https://doi.org/10.2118/221485-PA

Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

Panagiotis G. Asteris, Maria Karoglou, Athanasia D. Skentou, Graça Vasconcelos, Mingming He, Asterios Bakolas, Jian Zhou and Danial Jahed Armaghani
Ultrasonics 141 107347 (2024)
https://doi.org/10.1016/j.ultras.2024.107347

Concrete compressive strength prediction using an explainable boosting machine model

Gaoyang Liu and Bochao Sun
Case Studies in Construction Materials 18 e01845 (2023)
https://doi.org/10.1016/j.cscm.2023.e01845

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Jian Zhou, Yuxin Chen, Hui Chen, et al.
Frontiers in Public Health 11 (2023)
https://doi.org/10.3389/fpubh.2023.1119580

Influence of Striker Design on the Absorbed Energy of Low-Energy Reference Specimens Used for the Indirect Verification of Charpy Impact Testers

Johan Schuurmans
Journal of Testing and Evaluation 51 (5) 3574 (2023)
https://doi.org/10.1520/JTE20220515

A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique

Rakesh Kumar, Baboo Rai and Pijush Samui
Structural Concrete 24 (4) 5538 (2023)
https://doi.org/10.1002/suco.202200850

Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models

Hai-Van Thi Mai, May Huu Nguyen, Son Hoang Trinh and Hai-Bang Ly
Construction and Building Materials 369 130613 (2023)
https://doi.org/10.1016/j.conbuildmat.2023.130613

Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models

Athanasia D. Skentou, Abidhan Bardhan, Anna Mamou, et al.
Rock Mechanics and Rock Engineering 56 (1) 487 (2023)
https://doi.org/10.1007/s00603-022-03046-9

Modeling the Impact of Liquid Polymers on Concrete Stability in Terms of a Slump and Compressive Strength

Ahmed Salih Mohammed, Wael Emad, Warzer Sarwar Qadir, Rawaz Kurda, Kawan Ghafor and Raed Kadhim Faris
Applied Sciences 13 (2) 1208 (2023)
https://doi.org/10.3390/app13021208

Deep Neural Networks for the Estimation of Masonry Structures Failures under Rockfalls

Olga Mavrouli, Athanasia D. Skentou, Josep Maria Carbonell, et al.
Geosciences 13 (6) 156 (2023)
https://doi.org/10.3390/geosciences13060156

Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques

Tien-Thinh Le, Panagiotis G. Asteris and Minas E. Lemonis
Engineering with Computers 38 (S4) 3283 (2022)
https://doi.org/10.1007/s00366-021-01461-0

Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art

Chrysanthos Maraveas
Applied Sciences 13 (1) 14 (2022)
https://doi.org/10.3390/app13010014

CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure

Thanh-Hai Le, Hoang-Long Nguyen, Cao-Thang Pham, Huong-Giang Thi Hoang and Thuy-Anh Nguyen
Lecture Notes in Civil Engineering, CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure 203 1795 (2022)
https://doi.org/10.1007/978-981-16-7160-9_181

Forecast of Airblast Vibrations Induced by Blasting Using Support Vector Regression Optimized by the Grasshopper Optimization (SVR-GO) Technique

Lihua Chen, Panagiotis G. Asteris, Markos Z. Tsoukalas, Danial Jahed Armaghani, Dmitrii Vladimirovich Ulrikh and Mojtaba Yari
Applied Sciences 12 (19) 9805 (2022)
https://doi.org/10.3390/app12199805

Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures

Kang Peng, Menad Nait Amar, Hocine Ouaer, Mohammad Reza Motahari and Mahdi Hasanipanah
Engineering with Computers 38 (S1) 667 (2022)
https://doi.org/10.1007/s00366-020-01173-x

Compressive strength of efficient self‐compacting concrete with rice husk ash, fly ash, and calcium carbide waste additives using multiple artificial intelligence methods

Abdeliazim Mustafa Mohamed, Maaz Osman Bashir, Samir Dirar and Nisreen Beshir Osman
Structural Concrete 23 (4) 2523 (2022)
https://doi.org/10.1002/suco.202100286

Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members

Mohammad Sadegh Barkhordari, Mohammad Mahdi Barkhordari, Danial Jahed Armaghani, Ahmad Safuan A. Rashid and Dmitrii Vladimirovich Ulrikh
Sustainability 14 (19) 12041 (2022)
https://doi.org/10.3390/su141912041

Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure

Jinbi Ye, Juhriyansyah Dalle, Ramin Nezami, Mahdi Hasanipanah and Danial Jahed Armaghani
Engineering with Computers 38 (1) 497 (2022)
https://doi.org/10.1007/s00366-020-01085-w

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

Panagiotis G. Asteris, Athanasia D. Skentou, Abidhan Bardhan, Pijush Samui and Kypros Pilakoutas
Cement and Concrete Research 145 106449 (2021)
https://doi.org/10.1016/j.cemconres.2021.106449

Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings

Ahmed Salih Mohammed, Panagiotis G. Asteris, Mohammadreza Koopialipoor, Dimitrios E. Alexakis, Minas E. Lemonis and Danial Jahed Armaghani
Sustainability 13 (15) 8298 (2021)
https://doi.org/10.3390/su13158298

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Jin Duan, Panagiotis G. Asteris, Hoang Nguyen, Xuan-Nam Bui and Hossein Moayedi
Engineering with Computers 37 (4) 3329 (2021)
https://doi.org/10.1007/s00366-020-01003-0

Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

Hongjun Jing, Hima Nikafshan Rad, Mahdi Hasanipanah, Danial Jahed Armaghani and Sultan Noman Qasem
Engineering with Computers 37 (4) 2717 (2021)
https://doi.org/10.1007/s00366-020-00977-1

Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models

Hai-Bang Ly, Binh Thai Pham, Lu Minh Le, et al.
Neural Computing and Applications 33 (8) 3437 (2021)
https://doi.org/10.1007/s00521-020-05214-w

A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine

Danial Jahed Armaghani, Deepak Kumar, Pijush Samui, Mahdi Hasanipanah and Bishwajit Roy
Engineering with Computers 37 (4) 3221 (2021)
https://doi.org/10.1007/s00366-020-00997-x

Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling

Panagiotis G. Asteris, Minas E. Lemonis, Tien-Thinh Le and Konstantinos Daniel Tsavdaridis
Engineering Structures 248 113297 (2021)
https://doi.org/10.1016/j.engstruct.2021.113297

Prediction of cement-based mortars compressive strength using machine learning techniques

Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial J. Armaghani, Evgenios A. Kotsonis and Paulo B. Lourenço
Neural Computing and Applications 33 (19) 13089 (2021)
https://doi.org/10.1007/s00521-021-06004-8

Investigating the effective parameters on the risk levels of rockburst phenomena by developing a hybrid heuristic algorithm

Jian Zhou, Hongquan Guo, Mohammadreza Koopialipoor, Danial Jahed Armaghani and M. M. Tahir
Engineering with Computers 37 (3) 1679 (2021)
https://doi.org/10.1007/s00366-019-00908-9

Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

Jie Zeng, Panayiotis C. Roussis, Ahmed Salih Mohammed, Chrysanthos Maraveas, Seyed Alireza Fatemi, Danial Jahed Armaghani and Panagiotis G. Asteris
Applied Sciences 11 (8) 3705 (2021)
https://doi.org/10.3390/app11083705

Sulfonitrocarburizing of High-Speed Steel Cutting Tools: Kinetics and Performances

Mihai Ovidiu Cojocaru, Mihai Branzei, Sorin Ciuca, Ioana Arina Gherghescu, Mariana Ion, Leontin Nicolae Druga and Cosmin Mihai Cotrut
Materials 14 (24) 7779 (2021)
https://doi.org/10.3390/ma14247779

Artificial neural network modeling of sliding wear

Ivan I Argatov and Young S Chai
Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 235 (4) 748 (2021)
https://doi.org/10.1177/1350650120925582

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

Dong Van Dao, Hojjat Adeli, Hai-Bang Ly, Lu Minh Le, Vuong Minh Le, Tien-Thinh Le and Binh Thai Pham
Sustainability 12 (3) 830 (2020)
https://doi.org/10.3390/su12030830

Prediction of Surface Treatment Effects on the Tribological Performance of Tool Steels Using Artificial Neural Networks

Liborio Cavaleri, Panagiotis G. Asteris, Pandora P. Psyllaki, Maria G. Douvika, Athanasia D. Skentou and Nikolaos M. Vaxevanidis
Applied Sciences 9 (14) 2788 (2019)
https://doi.org/10.3390/app9142788

A Gene Expression Programming Model for Predicting Tunnel Convergence

Mohsen Hajihassani, Shahrum Shah Abdullah, Panagiotis G. Asteris and Danial Jahed Armaghani
Applied Sciences 9 (21) 4650 (2019)
https://doi.org/10.3390/app9214650

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Ly, Pham, Dao, et al.
Applied Sciences 9 (18) 3841 (2019)
https://doi.org/10.3390/app9183841

Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

Hoang-Long Nguyen, Thanh-Hai Le, Cao-Thang Pham, Tien-Thinh Le, Lanh Si Ho, Vuong Minh Le, Binh Thai Pham and Hai-Bang Ly
Applied Sciences 9 (15) 3172 (2019)
https://doi.org/10.3390/app9153172

Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis

Manh Duc Nguyen, Binh Thai Pham, Tran Thi Tuyen, et al.
The Open Construction and Building Technology Journal 13 (1) 178 (2019)
https://doi.org/10.2174/1874836801913010178

An Introduction to Wear Degradation Mechanisms of Surface-Protected Metallic Components

Pandora P. Psyllaki
Metals 9 (10) 1057 (2019)
https://doi.org/10.3390/met9101057

Compressive strength of natural hydraulic lime mortars using soft computing techniques

Maria Apostolopoulou, Danial J. Armaghani, Asterios Bakolas, et al.
Procedia Structural Integrity 17 914 (2019)
https://doi.org/10.1016/j.prostr.2019.08.122

Soft computing-based techniques for concrete beams shear strength

Danial J. Armaghani, George D. Hatzigeorgiou, Chrysoula Karamani, et al.
Procedia Structural Integrity 17 924 (2019)
https://doi.org/10.1016/j.prostr.2019.08.123