Dr. Mena Souliman Profile

Dr. Mena Souliman

at Univ of Texas at Tyler

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Publications (2)

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Proceedings Article | 9 May 2024 Presentation + Paper

Integrating piezoelectric sensors for enhanced failure prediction of residential buildings in hurricane

Fedaa Ali, Leila Donyaparastlivari, Syed Ali Ghorshi, Abolghassem Zabihollah, Alwathiqbellah Ibrahim, Mena Souliman

Proceedings Volume 12949, 1294905 (2024) https://doi.org/10.1117/12.3010054

KEYWORDS: Sensors, Buildings, Structural health monitoring, Hurricanes, Signal processing, Fourier transforms, Vibration, Telecommunications, Reliability, Failure analysis

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Coastal regions, particularly in the southeastern United States, are consistently confronted with the ongoing threat of hurricane-induced damage to their buildings and civil infrastructure. Consequently, there is a pressing need to concentrate efforts on the evaluation and prediction of structural integrity and reliability in such environments. This is paramount for minimizing losses and enhancing public safety in the face of these challenging climatic conditions. Current structural health monitoring systems are typically customized for specific buildings, rendering them excessively expensive and impractical for residential structures. This research presents a comprehensive feasibility study for an economical yet efficient system designed to anticipate potential failures and assess the safety and reliability of residential buildings. The proposed system employs integrated piezoelectric sensors to monitor alterations in the structural and material characteristics of building components. The collected sensor data may be transmitted to a mobile application using a WiFi or Bluetooth system. To validate the functionality of this innovative system, a proof-of-concept prototype building was constructed utilizing additive manufacturing, featuring integrated piezoelectric sensors. The system underwent experimental testing under base excitation at various frequencies, revealing distinct output variations at different locations. This substantiates the feasibility of employing integrated piezoelectric sensors within structural buildings for effective structural health monitoring. The collected data will serve as a foundational resource for accurately estimating the building’s reliability in anticipation of future hurricane events.

Proceedings Article | 13 June 2023 Presentation + Paper

Estimation of road surface quality using real-time video analysis

Mukul Shirvaikar, Mena Souliman, Samer Dessouky

Proceedings Volume 12528, 1252806 (2023) https://doi.org/10.1117/12.2664895

KEYWORDS: Roads, Video, Sensors, Vibration, Accelerometers, Gyroscopes, Cameras, Transportation, Image processing, Global Positioning System

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An accurate and consistent survey of road surface distresses is critical for pavement rehabilitation design and management, allowing public managers to maximize the value of constantly limited budgets for road improvements and maintenance. Manual pavement distress surveys are time-consuming, costly, and dangerous on heavily traveled highways. Automated surveys using video recording hardware devices have been developed and improved over the years, to solve the problems associated with manual surveys. However, reliable distress detection software and data analysis remain difficult. With the advances in smartphone technology, it is now possible to use mounted devices in the field effectively for such applications. A smartphone application was previously developed to utilize on-board accelerometer, gyroscope, and GPS sensors, along with software derived signals from the same sensors, to sample vibrational and geolocation datasets to capture pavement distresses such as potholes when mounted in a standardized configuration in a vehicle. This study examines the possibility of using real-time video processing for pavement surface quality detection. Video captured from the mounted camera is analyzed to estimate road conditions and correlated with sensor data ground truth to corroborate the efficacy of the technique. The findings of this study could indeed aid in developing more effective uses of specialized software for pavement condition classification, to assist decision makers in selecting solutions based on budget and desired survey accuracy, and to evaluate how existing devices will perform when used with the developed algorithm.

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