| Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN E Momeni, R Nazir, DJ Armaghani, H Maizir Measurement 57, 122-131, 2014 | 421 | 2014 |
| Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition DJ Armaghani, ET Mohamad, MS Narayanasamy, N Narita, S Yagiz Tunnelling and Underground Space Technology 63, 29-43, 2017 | 414 | 2017 |
| Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization DJ Armaghani, M Hajihassani, ET Mohamad, A Marto, SA Noorani Arabian Journal of Geosciences 7, 5383-5396, 2014 | 400 | 2014 |
| Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks E Momeni, DJ Armaghani, M Hajihassani, MFM Amin Measurement 60, 50-63, 2015 | 381 | 2015 |
| A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength DJ Armaghani, PG Asteris Neural Computing and Applications 33 (9), 4501-4532, 2021 | 378 | 2021 |
| Prediction of seismic slope stability through combination of particle swarm optimization and neural network B Gordan, D Jahed Armaghani, M Hajihassani, M Monjezi Engineering with computers 32, 85-97, 2016 | 372 | 2016 |
| Feasibility of indirect determination of blast induced ground vibration based on support vector machine M Hasanipanah, M Monjezi, A Shahnazar, DJ Armaghani, A Farazmand Measurement 75, 289-297, 2015 | 296 | 2015 |
| Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm M Hajihassani, DJ Armaghani, A Marto, ET Mohamad Bull Eng Geol Environ 74 (3), 873-886, 2015 | 280 | 2015 |
| Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate J Zhou, Y Qiu, S Zhu, DJ Armaghani, C Li, H Nguyen, S Yagiz Engineering Applications of Artificial Intelligence 97, 104015, 2021 | 276 | 2021 |
| Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling M Hasanipanah, M Noorian-Bidgoli, D Jahed Armaghani, H Khamesi Engineering with Computers 32, 705-715, 2016 | 273 | 2016 |
| Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques J Zhou, Y Qiu, DJ Armaghani, W Zhang, C Li, S Zhu, R Tarinejad Geoscience Frontiers 12 (3), 101091, 2021 | 245 | 2021 |
| Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach ET Mohamad, D Jahed Armaghani, E Momeni, ... Bulletin of Engineering Geology and the Environment 74, 745-757, 2015 | 243 | 2015 |
| Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm E Ebrahimi, M Monjezi, MR Khalesi, DJ Armaghani Bulletin of Engineering Geology and the Environment 75, 27-36, 2016 | 238 | 2016 |
| Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization M Hajihassani, DJ Armaghani, H Sohaei, ET Mohamad, A Marto Applied Acoustics 80, 57-67, 2014 | 232 | 2014 |
| Random forests and cubist algorithms for predicting shear strengths of rockfill materials J Zhou, E Li, H Wei, C Li, Q Qiao, DJ Armaghani Applied sciences 9 (8), 1621, 2019 | 230 | 2019 |
| Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate H Xu, J Zhou, P G. Asteris, D Jahed Armaghani, MM Tahir Applied sciences 9 (18), 3715, 2019 | 224 | 2019 |
| Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles D Jahed Armaghani, RSNSBR Shoib, K Faizi, ASA Rashid Neural Computing and Applications 28, 391-405, 2017 | 223 | 2017 |
| Application of several optimization techniques for estimating TBM advance rate in granitic rocks DJ Armaghani, M Koopialipoor, A Marto, S Yagiz Journal of Rock Mechanics and Geotechnical Engineering 11 (4), 779-789, 2019 | 217 | 2019 |
| Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions M Koopialipoor, D Jahed Armaghani, A Hedayat, A Marto, B Gordan Soft Computing 23, 5913-5929, 2019 | 217 | 2019 |
| An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite D Jahed Armaghani, E Tonnizam Mohamad, E Momeni, ... Bulletin of engineering geology and the environment 74, 1301-1319, 2015 | 209 | 2015 |
Prof. Dr. Edy Tonnizam MohamadProfessor, Centre of Tropical Geoengineering (GEOTROPIK), Faculty of Civil Engineering, UTMEmail verificata su utm.my