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Engineering and Technology

D-Index
54
Citations
8942
World Ranking
3267
National Ranking
175

Overview

Manoj Khandelwal is affiliated with Federation University Australia. Their research primarily focuses on engineering, with a specialization in several subfields including civil and structural engineering, mechanics of materials, mechanical engineering, safety, risk, reliability and quality, and ocean engineering.

The researcher has contributed extensively to the study of rock mechanics and modeling, mineral processing and grinding, tunneling and rock mechanics, landslides and related hazards, geotechnical engineering and analysis, drilling and well engineering, and dam engineering and safety.

Frequent publication venues for Manoj Khandelwal include:

  • Geomechanics and Geophysics for Geo-Energy and Geo-Resources
  • Engineering With Computers
  • Journal of Rock Mechanics and Geotechnical Engineering
  • Natural Resources Research
  • Tunnelling and Underground Space Technology

Their frequent collaborators include Jian Zhou, Danial Jahed Armaghani, Moshood Onifade, Yingui Qiu, and Kun Du.

Some of the recent papers authored or co-authored by Manoj Khandelwal are:

  • Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration, 2021, Engineering With Computers
  • Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization, 2020, Underground Space
  • Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations, 2021, International Journal of Rock Mechanics and Mining Sciences
  • Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms, 2021, Journal of Rock Mechanics and Geotechnical Engineering
  • Advancing toward sustainability: The emergence of green mining technologies and practices, 2024, Green and Smart Mining Engineering

Best Publications

  • Prediction of blast-induced ground vibration using artificial neural network

    Manoj Khandelwal;T.N. Singh

  • Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration

    Yingui Qiu;Jian Zhou;Manoj Khandelwal;Haitao Yang

  • Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach

    Manoj Khandelwal;T.N. Singh

  • Evaluation of blast-induced ground vibration predictors

    Manoj Khandelwal;T.N. Singh

  • Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network

    Masoud Monjezi;Mahdi Hasanipanah;Manoj Khandelwal

  • Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization

    Jian Zhou;Yingui Qiu;Shuangli Zhu;Danial Jahed Armaghani

  • Correlating static properties of coal measures rocks with P-wave velocity

    Manoj Khandelwal;T.N. Singh

  • Correlating P-wave Velocity with the Physico-Mechanical Properties of Different Rocks

    Manoj Khandelwal

  • An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran

    Mahdi Saadat;Manoj Khandelwal;M. Monjezi

  • A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

    A. Sayadi;M. Monjezi;N. Talebi;Manoj Khandelwal

  • Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations

    Jian Zhou;Yingui Qiu;Manoj Khandelwal;Shuangli Zhu

  • Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting

    Danial Jahed Armaghani;Ehsan Momeni;Seyed Vahid Alavi Nezhad Khalil Abad;Manoj Khandelwal

  • Evaluation and prediction of blast-induced ground vibration using support vector machine

    Manoj Khandelwal

  • Evaluation and prediction of blast induced ground vibration using support vector machine

    M Khandelwal;Pk Kankar;Sp Harsha

  • Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method

    Manoj Khandelwal;M. Monjezi

  • An experimental study on tensile characteristics of granite rocks exposed to different high-temperature treatments

    W. G. P. Kumari;D. M. Beaumont;P. G. Ranjith;M. S. A. Perera;M. S. A. Perera

  • Application of soft computing to predict blast-induced ground vibration

    Manoj Khandelwal;D. Lalit Kumar;Mohan Yellishetty

  • Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique

    Manoj Khandelwal;Danial Jahed Armaghani

  • Correlating index properties of rocks with P-wave measurements

    Manoj Khandelwal;Pathegama Ranjith

  • Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

    Enming Li;Enming Li;Fenghao Yang;Meiheng Ren;Xiliang Zhang

  • Prediction of Blast Induced Air Overpressure in Opencast Mine

    Manoj Khandelwal;T. N. Singh

  • A correlation between Schmidt hammer rebound numbers with impact strength index, slake durability index and P-wave velocity

    P. K. Sharma;P. K. Sharma;Manoj Khandelwal;T. N. Singh

Frequent Co-Authors

Masoud Monjezi
Masoud Monjezi Tarbiat Modares University
T. N. Singh
T. N. Singh Indian Institute of Technology Bombay
Danial Jahed Armaghani
Danial Jahed Armaghani University of Technology Sydney
Pathegama Gamage Ranjith
Pathegama Gamage Ranjith Monash University
Mahdi Hasanipanah
Mahdi Hasanipanah Duy Tan University
Muhd Zaimi Abd Majid
Muhd Zaimi Abd Majid University of Technology Malaysia
Panagiotis G. Asteris
Panagiotis G. Asteris School of Pedagogical and Technological Education
Mandadige Samintha Anne Perera
Mandadige Samintha Anne Perera University of Melbourne
Mohammadreza Koopialipoor
Mohammadreza Koopialipoor Amirkabir University of Technology
Pavan Kumar Kankar
Pavan Kumar Kankar Indian Institute of Technology Indore

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