Mohammed Nayeemuddin

About

Dr. Mohd Nayeemuddin is a faculty in the department of civil engineering at Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia and held other previous positions as a Lecturer in the Department of Civil Engineering at Khaja Banda Nawaz University, Karnataka, India. He has more than 14 years of academic and research experience. Completed PhD in Civil Environmental Engineering from Universiti Sains Malaysia in the year 2025. Mohd Nayeemuddin has completed his Master in Technology in Civil Structural Engineering from Khaja Banda Nawaz University, Karnataka, India. His research interest in the area of RCC design of buildings, structural steel design, Foundation design, applying seismic earthquake and wind load by using structural application like ETABS, STAAD PRO, Safe, SAP 2000, Auto CAD, RCDC, Prokon, Primavera Software. Under Environmental Engineering such as, treatment of seawater, pollutants removal from seawater and oil produced water under the solar and artificial source of light. Application of MATLAB (Artificial Neural Network) tool, Fuzzy analysis, regression model, optimization, ANOVA, variance, back propagation methods, genetic algorithm, Levenberg Marquardt method. Statistical analysis with response surface methodology optimization, simulation for experimental variables of seawater samples. Mohd Nayeemuddin has more than nearly 47 publications to his credits in Journals, Conference and chapters of International reputed and has total 14 years of experience in teaching and 4 years of experience in industry. In addition, he guided more than 30 engineering students for the senior design projects in civil engineering department.

Employment

• Prince Mohammad bin Fahd University Faculty in the Department of Civil Engineering
  2017 - Present

Education

• Universiti Sains Malaysia PhD in Environmental Engineering
  2020 - 2025

Projects & Funding

Projects & funding information is unavailable.

Publications (78)

• Artificial intelligence and machine learning in sustainable sand production
  2027 DOI: 10.1016/B978-0-443-36558-4.00002-5
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• Recycled PET waste sand concrete for the construction industry: A case study
  2027 DOI: 10.1016/B978-0-443-36558-4.00014-1
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• Evaluating the Performance of Traditional Predictive Models Against Advanced Machine Learning Techniques for the Removal of Copper Using Low Cost Adsorbent
  Process Integration and Optimization for Sustainability 2026 DOI: 10.1007/s41660-026-00734-9
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• Experimental and Machine Learning-Based Prediction of Flow Velocity in Open Channels Using RNN, CatBoost, and MGO-RF
  Flow Measurement and Instrumentation 2026 DOI: 10.1016/j.flowmeasinst.2026.103715
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• A Comparative Evaluation of GRNN and OSELM Models for Dynamic Pressure Drop Prediction in Deep Bed Filtration Systems
  Engineering, Technology & Applied Science Research 2026 DOI: 10.48084/etasr.18953
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• Resilient Modulus Prediction for Fine-Grained Soils Using a Novel Voting-Based Ensemble Approach: Integrating Gradient Boosting and Multilayer Perceptron Neural Networks
  International Journal of Pavement Research and Technology 2026 DOI: 10.1007/s42947-025-00653-x
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• Field Data–Driven Machine-Learning Approach for Estimating Pavement Skid Numbers from LWST Measurements for Improved Asphalt Pavement Safety
  Journal of Transportation Engineering, Part B: Pavements 2026 DOI: 10.1061/JPEODX.PVENG-1822
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• Solar photocatalytic degradation of seawater organic pollutants using RSM process optimization and advanced machine learning modeling
  Water Supply 2026 DOI: 10.2166/ws.2026.200
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• An Artificial Rabbit Optimization-Enhanced Support Vector Regression Framework for Sustainable Solar Photocatalytic Water Treatment
  Catalysts 2026 DOI: 10.3390/catal16090798
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• A Stacked Bidirectional Long Short-Term Memory (BiLSTM) Network to Predict Phase Angle and Complex Shear Modulus of the Superpave Dynamic Shear Rheometer
  Iranian Journal of Science and Technology, Transactions of Civil Engineering 2026 DOI: 10.1007/s40996-025-02015-w
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• Innovative Soft Computing Techniques for Analyzing Rate Constants in Artificial UV-Driven Photocatalysis Within Tubular Reactors
  Nayeemuddin Mohammed, Diaa S. Metwally, Borhen Louhichi +4 more
  Catalysts 2026 DOI: 10.3390/catal16070662
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• Development of a hybrid approach for real-time SOC estimation in renewable energy systems
  Engineering Research Express 2026 DOI: 10.1088/2631-8695/ae7fcc
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• Evaluation of surface and durability characteristics of ready-mixed concrete under various curing conditions
  Innovative Infrastructure Solutions 2026 DOI: 10.1007/s41062-026-02764-1
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• Forecasting and Optimization of Pressure Loss Using Soft Computing Methods in a Submerged Multi-nozzle Electrolyte Flow System
  Journal of The Institution of Engineers (India): Series E 2026 DOI: 10.1007/s40034-026-00325-w
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• Integrating Soil Index Parameters and Machine Learning for Reliable California Bearing Ratio Estimation using a GA-ELM Model
  International Journal of Pavement Research and Technology 2026 DOI: 10.1007/s42947-024-00489-x
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• Exploring the field of polymeric photocatalysis: Overcoming challenges, embracing opportunities, and envisioning future directions – A comprehensive review
  Interactions 2026 DOI: 10.1007/s10751-026-02372-6
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• Experimental and machine learning optimization of centrifugal pump performance
  Flow Measurement and Instrumentation 2026 DOI: 10.1016/j.flowmeasinst.2025.103048
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• Advanced Machine Learning for Sustainable Concrete Strength Prediction and Resource Optimization
  Sdhm Structural Durability and Health Monitoring 2026 DOI: 10.32604/sdhm.2026.080495
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• Advancing Smart Grid Security and Stability Through Machine Learning Techniques
  Materials Research Proceedings 2026 DOI: 10.21741/9781644904091-33
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• Data Driven ExtraTrees-Based State-of-Charge Estimation for New and Aged Li-ion Battery Cells
  Materials Research Proceedings 2026 DOI: 10.21741/9781644904091-56
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