9285
salqunaynah@pmu.edu.sa
S015
Dr. Siraj M. Al Qunaynah is a Tenure-Track Assistant Professor of Civil Engineering in the Department of Civil Engineering, College of Engineering, at Prince Mohammad Bin Fahd University (PMU), Al Khobar, Saudi Arabia.
He earned his Ph.D. in Civil Engineering from Iowa State University, Ames, Iowa, United States, where his research focused on improving the sustainability and performance of lightweight concrete through waste material utilization, data-driven methods, and artificial intelligence (AI). He also holds an M.S. in Civil Engineering, with a concentration in Structural Engineering, from the University of Dayton, United States, and a B.S. in Civil Engineering from King Saud University, Saudi Arabia.
Dr. Al Qunaynah previously served as an interdisciplinary researcher in Computer Engineering at Iowa State University, where his research bridged civil engineering, computer engineering, artificial intelligence, and emerging digital technologies. His interdisciplinary work explored machine learning, data-driven modeling, synthetic data generation, digital twins, wireless communication, the Internet of Things (IoT), quantum technologies, advanced sensing, and computational modeling. His research interests encompass applications of these technologies in 3D concrete printing (3DCP) automation, intelligent construction systems, cement and cementitious material optimization, structural health monitoring (SHM), and sustainable infrastructure. He has also collaborated with researchers at other universities on research involving low-carbon cements and advanced cementitious materials.
In addition to his academic and research experience, Dr. Al Qunaynah brings approximately 10 years of professional experience in the engineering and construction sectors, with expertise in engineering design, structural analysis, inspection, technical evaluation, construction supervision, and project management. His experience includes domestic and international projects involving high-rise buildings, bridges, viaducts, segmental bridge construction using launching gantries, post-tensioned concrete structures, roads, metro systems, and other large-scale infrastructure developments.
Dr. Al Qunaynah is a member of the American Concrete Institute (ACI), the American Society of Civil Engineers (ASCE), ASTM International, and the Tau Beta Pi Engineering Honor Society. His professional affiliations reflect his commitment to engineering excellence, technical standards, professional development, and continued engagement with the engineering and scientific communities.
His academic and professional interests focus on advancing sustainable construction materials, intelligent infrastructure, and emerging engineering technologies through interdisciplinary research, education, and industry collaboration. He is particularly interested in integrating artificial intelligence, digital twins, advanced sensing, and automation with concrete materials and construction processes to develop more sustainable, resilient, and intelligent infrastructure systems.
PhD in Civil Engineering (Materials), Iowa State University, Ames, Iowa at USA.
Dependable Data Driven Discovery National Research Traineeship (NRT D4),Iowa State University, Ames, Iowa USA.
M.S., Civil Engineering (Structural Engineering), University of Dayton, Dayton, Ohio USA.
BS, Civil Engineering (Structure Engineering), King Saud University, Riyadh, Saudi Arabia.
Prince Mohammad Bin Fahd University (PMU)
Centre for Sustainable Infrastructure Materials (SIM) at Prince Mohammad Bin Fahd University (PMU)
Iowa State University, Ames, Iowa, USA.
His research interests aim to bridge the gap between academia and industry and include sustainable, low- and zero-carbon cement, cementitious and construction materials, the development and manufacture of cement-free materials, foamed and lightweight concrete, waste valorization, smart materials, and self-healing concrete. His interests further encompass three-dimensional concrete printing (3DCP) and automation, artificial intelligence, machine learning, deep learning, data science, digital twins, and wireless communication applications for civil infrastructure. His research connects materials innovation with emerging technologies to advance practical solutions for a more sustainable, resilient, and intelligent built environment.
