Dr. Zaka Emad, a distinguished expert in Mining Engineering, joined the College of Petroleum Engineering and Geosciences (CPG) at KFUPM in November 2024, bringing a wealth of expertise and passion for innovation in the field. Before this, he spent a decade shaping minds at UET Lahore, focusing on underground mine design, rock mechanics, and rock fragmentation.
His impressive postdoctoral research at Creighton Mine in Sudbury, Canada, explored numerical modeling of destress blasting, while his Ph.D. work delved into the dynamic performance of cemented rockfill under blast-induced vibrations. With over 19 years of teaching and research experience, Dr. Emad is a pioneer in underground mining, excavation techniques, rock mechanics, mine safety, and numerical modeling. His commitment to advancing knowledge and solving complex challenges makes him a vital asset to the CPG and the broader mining community.
EDUCATION
Doctor of Philosophy in Mining Engineering
RESEARCH, TEACHING, or OTHER INTERESTS
Engineering, General Earth and Planetary Sciences, Multidisciplinary, Building and Construction
Rapidly Synthesized Microwave-Sintered Geopolymer Foam Utilizing Granite Waste: A Sustainable Approach for High-Performance Construction Materials Tooba Shafiq, Faseeh U. Rehman Khokhar, Ehsan Ul Haq, Muhammad Zaka Emad, Syed Farhan Raza, Rana Muhammad Asad Khan Sustainability Switzerland, 2026 This study presents a novel, rapidly synthesized geopolymer foam fabricated from granite industrial waste using microwave sintering, reducing the demolding time from 7 days to 3 min and the overall processing time to 24 h, while enhancing mechanical performance. Five sample compositions (G1–G5) were prepared with varying granite powder and alkaline solution ratios, cured in a microwave for 3 min, and sintered for an additional 3 min. X-ray fluorescence (XRF), compressive strength tests, water absorption, thermogravimetric analysis (TGA), differential thermal analysis (DTA), and Fourier transform infrared spectroscopy (FTIR) were used for thorough characterization. The compressive strength increased progressively from 13 MPa (G1) to 20 MPa (G5), the total porosity decreased from 33.33% to 18.58%, the water absorption reached a minimum of 2.02% (G5), and the bulk density rose from 1.143 to 1.49 g/cm3. XRF analysis confirmed Si/Al molar ratios of 6.5–11.4, indicating enhanced aluminosilicate network development. FTIR confirmed progressive geopolymerization, with integrated Si-O-T band areas increasing from 41,900 a.u. (G1) to 44,680 a.u. (G5). The microwave sintering approach consumed over 90% less active energy than conventional thermal curing, significantly reducing associated CO2 emissions and thereby supporting SDG 7, SDG 12, and SDG 13. These results position granite-waste-derived geopolymer foam as a high-performance, energy-efficient alternative to conventional fired bricks and cement-based construction materials.
Economic and Engineering Evaluation of Granitoid Rocks in the Western Himalayas, Northern Pakistan Syed Najm Ul Hassan Sayyad Kazmi, Muhammad Saleem Mughal, Abdul Muntaqim Naji, Khawaja Umair Majeed, Johar Ali, Fahad Hameed, Muhammad Rustam Khan, Iman Ayoobi, Muhammad Zaka Emad Economic and Environmental Geology, 2026 Syed Najm Ul Hassan Sayyad Kazmi, Muhammad Saleem Mughal, Abdul Muntaqim Naji, Khawaja Umair Majeed, Johar Ali, Fahad Hameed, Muhammad Rustam Khan, Iman Ayoobi, Muhammad Zaka Emad. Econ. Environ. Geol. 2026;59:251-72. https://doi.org/10.9719/EEG.2026.59.2.251
Role of constrained modulus in predicting resilient modulus of RAP-included granular materials via ML and conventional methods Muhammad Arshad, Muhammad Zaka Emad Results in Engineering, 2026 • Benchmarked six modeling techniques (MLR, NLR, ANN, RNN, RF, DT) using a large dataset of 540 experimentally determined MR values, highlighting strengths and limitations across both conventional and machine learning methods. • ANN outperformed all other models with R² up to 0.94 and MSE as low as 445, while RNN showed inferior accuracy and higher underprediction bias. • Explicit, closed-form ANN and RNN equations were extracted from trained weights and biases, enhancing the interpretability of traditionally black-box ML models. • Integrated the constrained modulus (Mc) and RAP content as key predictors in developing models to estimate the resilient modulus (M R ) of unbound granular materials used in pavement layers. • Comprehensive sensitivity analysis using permutation, perturbation, and feature importance identified confining pressure (σ₃) and OMC as the most influential input variables. • Model simplification by excluding low-impact variables reduced complexity with minimal loss of accuracy, supporting efficient and sustainable pavement design using RAP materials. . Conventional regression and modern machine learning (ML) models were developed and evaluated to estimate the resilient modulus (M R ) of unbound granular materials. Recycled asphalt pavement (RAP) content and constrained modulus (M c ) values were included as pivotal input variables. The predictive accuracy of ML models like artificial neural networks (ANN), recurrent neural networks (RNN), random forests (RF), and decision trees (DT) with traditional multilinear regression (MLR) and nonlinear regression (NLR) models was assessed using standard statistical invariants, including R², MSE and residual error. The MLR model had R² values of 0.894 (training) and 0.886 (testing), whereas the corresponding values for the NLR models were 0.959 and 0.958. For ANN-based algorithms, a single-hidden-layer model with 13 neurons performed best, with R² values of 0.908 and 0.900 and MSE of 682 and 708 during training and testing phases. RNN models were fairly accurate but not as excellent as ANN-based models. RF and DT both achieved an R² of 1 for the training dataset, and for the testing datasets, this index remained 0.936 and 0.961 for the best performing structure with ‘N-estimator = 9’ and at a ‘Tree Depth = 13’, respectively. Various sensitivity analysis techniques identified varying degrees of relative importance for the input variables, practically depending upon the modeling techniques. However, collectively, σ 3 and gravel content proved to be the most and least contributing factors in the model’s development and accuracy. Closed-form predictive models based on ANN internal structures yielded R² = 0.884∼0.903 and outperformed RNN algorithms with R² = 0.596∼0.641.
Integrated geophysical and aerial photogrammetry approach for detailed delineation of iron ore mineralization in lateritic deposits Muhammad Junaid, Furqan Khan, Badee Alshameri, Sami Ullah Shah, Muhammad Zaka Emad, Tariq Feroze Mining of Mineral Deposits, 2026 Purpose. This study integrates unmanned aerial vehicles (UAVs) and Magnetic and 2D Electrical Resistivity Tomography (2D ERT) surveys to delineate subsurface laterite ore mineralization zones while reducing exploration costs. UAV imagery enabled the reconstruction of high-resolution digital elevation models and orthomosaics, providing detailed topographic information for survey planning. Methods. Magnetic survey integrated with 2D electrical resistivity tomography (ERT) profiles were applied to characterize subsurface lithology and identify layers such as topsoil, shale, and laterite. Findings. The ERT survey reveals that the topsoil is 3 meters thick and has a resistivity range of 10-50 Ω.m. The resistivity of shale varied between 50 and 150 Ω.m, with a thickness of two meters. The laterite ore was identified with resistivity values between 150 and 1200 Ω·m and a thickness of 5 m. Magnetic surveys identified magnetic anomalies of 200-600 nT, estimated at a depth of 3-5 m using forward modeling. Regional-scale interpretations from total magnetic intensity (TMI), reduced-to-pole (RTP), and continuation maps highlighted the detailed distribution of the magnetic anomalies throughout the study area, lithological variations, fault systems, and deep-seated magnetized bodies. Originality. This study demonstrates an integrated, low-cost workflow for lateritic mineralization that uses detailed geophysical data, including magnetic methods, 2D ERT, and UAV photogrammetry. Conventional techniques are quite expensive relative to the value of the ore. Practical implications. The results demonstrate that the integration of aerial photogrammetry, magnetic surveys, and 2D electrical resistivity tomography provides an efficient and cost-effective approach for delineating laterite ore mineralization zones and can serve as a viable alternative to conventional exploration methods.
Ergonomic selection of stemming plugs for quarry blasting operation 2019 SME Annual Conference and Expo and Cma 121st National Western Mining Conference, 2019
Numerical modeling of main and tail gate of a modified long wall mining operation SME Annual Conference and Expo 2017 Creating Value in A Cyclical Environment, 2017
3D modelling of mine backfill Proceedings of the 36th International Conference on Ground Control in Mining Icgcm 2017, 2017
Modelling dynamic loading on backfilled stopes in sublevel stoping systems Rock Characterisation Modelling and Engineering Design Methods Proceedings of the 3rd ISRM Sinorock 2013 Symposium, 2013
Modelling dynamic loading on backfilled stopes in sublevel stoping systems Rock Characterisation Modelling and Engineering Design Methods, 2013