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Edy Tonnizam Mohamad
Researcher at Universiti Teknologi Malaysia
Publications - 127
Citations - 4566
Edy Tonnizam Mohamad is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Rock mass classification & Artificial neural network. The author has an hindex of 32, co-authored 112 publications receiving 3251 citations.
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Journal ArticleDOI
Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
Danial Jahed Armaghani,Edy Tonnizam Mohamad,Mogana Sundaram Narayanasamy,Nobuya Narita,Saffet Yagiz +4 more
TL;DR: In this article, the authors developed new intelligent prediction models for estimating the tunnel boring machine performance (TBM) by means of the rate pf penetration (PR) of the Pahang-Selangor Raw Water Transfer (PSRWT) tunnel in Malaysia.
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Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
TL;DR: In this article, a hybrid artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) was proposed to predict peak particle velocity (PPV) resulting from quarry blasting.
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Improvement of Problematic Soils with Biopolymer—An Environmentally Friendly Soil Stabilizer
Nima Latifi,Suksun Horpibulsuk,Christopher L. Meehan,Muhd Zaimi Abd Majid,Mahmood Md Tahir,Edy Tonnizam Mohamad +5 more
TL;DR: In this paper, the authors used traditional chemical stabilizing additives such as cement and lime to improve the engineering performance of problematic soils with high compressibility and low shear strength.
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Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach
Edy Tonnizam Mohamad,Danial Jahed Armaghani,Ehsan Momeni,Seyed Vahid Alavi Nezhad Khalil Abad +3 more
TL;DR: The high performance indices of the proposed model highlight the superiority of the PSO-based ANN model for UCS prediction, which is widely accepted that optimization algorithms such as particle swarm optimization can improve ANN performance.
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An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite
Danial Jahed Armaghani,Edy Tonnizam Mohamad,Ehsan Momeni,Mogana Sundaram Narayanasamy,Mohd For Mohd Amin +4 more
TL;DR: This study aims to present two predictive models of UCS and E for granite using an adaptive neuro-fuzzy inference system (ANFIS) and found that the ANFIS predictive model of UCS, with R2, RMSE and VAF equal to 0.2, outperforms the MRA and ANN models.