Muhammad Safeer Khan

Papers

Total Citations

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About

Muhammad Safeer Khan is a researcher focused on the intersection of robotics, acoustics, and deep learning for the structural health monitoring of critical underground infrastructure. His work addresses the urgent challenge of detecting cracks, blockages, and potential overflows in aging sewer pipe networks before they lead to environmental contamination and public health hazards. Khan’s most-cited paper, “Statistical Analysis of Acoustic Response of PVC Pipes for Crack Detection” (10 citations), pioneers the use of sound-based diagnostics as a complement to the industry-standard CCTV inspection. He further advanced this field by developing an acoustic approach to mitigate sewer system overflows (6 citations) and by integrating robotics with deep learning frameworks (3 citations) to automate and enhance the accuracy of pipe condition assessments. By combining robotic crawlers with intelligent data analysis, Khan’s research offers a more proactive, cost-effective alternative to manual video review, promising to reduce the risk of catastrophic pipe failures and protect water resources.