Papers
3
Total Citations
166
H-Index
2
About
Mohamad Alipour is a leading researcher at the intersection of civil infrastructure, machine learning, and non-destructive evaluation. His work focuses on developing robust, human-centered sensing platforms that automate the visual and sensor-based inspection of critical infrastructure, from bridges and buildings to hazardous environments. Alipour’s most impactful contribution is his pioneering research on material-specific deep learning models for crack detection, demonstrating that models trained on one material (e.g., concrete) can be adapted for others (e.g., steel or asphalt) without sacrificing accuracy. This work, published in 2020, has garnered 147 citations, underscoring its significance in advancing practical, scalable inspection tools. He has also applied machine learning to landmine detection using metal detector data, achieving a 17-citation study that aims to reduce false-alarm rates and improve safety for deminers. His ongoing work on human sensing platforms for digitizing visual inspections of critical infrastructure (2023) reflects his commitment to bridging computer vision, robotics, and real-world engineering challenges. Alipour’s research is distinguished by its focus on robustness and transferability, ensuring that AI-driven inspection tools are not just accurate in the lab but reliable in the field.
Research Focus
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Top Papers
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