Structural Monitoring by Analyzing Response Trend (SMART) Lab

Welcome to the SMART Lab directed by Dr. Anwarul Islam, Professor of Civil Engineering at Youngstown State University.

Health Monitoring of Structures using Wireless Sensor Networks

The SMART Lab uses advanced monitoring techniques and wireless sensor netwroks (WSN) to collect and analyze response of structures under various loads. This state-of-the-art WSN technology has advanced bridge diagnostics and long-term monitoring of structures to a new level. Using this technology, condition assessment and load rating of a bridge can be performed at a fraction of  a cost and time required in traditional methods.

Bridge Load Rating using Dynamic Response Collected through WSN

This research describes a method for load rating of prestressed box beam (PSBB) bridges based on their dynamic response collected using wireless sensor networks (WSNs). Although the percentage of deficient bridges in the United States has been declining slowly, a significant portion is still closed to traffic or posted with load restrictions. An accurate load rating of bridges is very expensive; therefore, new tools for quick, efficient and response-based load rating of bridges will save time and money. The hypothesis is based on the assumption that the health of a bridge is associated with its vibration signatures under vehicular loads. Two WSNs were deployed on a 25-year old PSBB bridge under trucks with variable loads and speeds for collecting real-time dynamic response at the current condition. Dynamic simulations of three dimensional finite element models of a bridge were performed to acquire its dynamic response under vehicular loads at its newest condition right after construction. The bridge model was validated by field testing and numerical analysis. Fast Fourier Transform and peak-picking algorithms were used to find maximum peak amplitudes and their corresponding frequencies. This information and the necessary bridge geometric parameters were used to calculate the in-service stiffness of the bridge in order to develop application software for load rating of bridges. The application software can instantly calculate the load rating of a PSBB bridge by collecting its real time dynamic response under vehicular loads using WSNs. The research outcome and the software will help reduce bridge maintenance costs and will increase public safety.

Bridge Condition Assessment using Dynamic Response Collected through WSN

With a large inventory of deficient and aging bridges in the United States, this research focused on developing dynamic response based health monitoring system of prestressed box beam (PSBB) bridges that will provide more realistic and cost-efficient results. The hypothesis is based on the assumption that the dynamic response is a sensitive and important indicator of the physical integrity and condition of a structure. Two wireless sensor networks (WSNs) were deployed for the collection of real-time acceleration response of a 25-year old PSBB bridge under trucks with variable loads and speeds. The acceleration response of the bridge at its newest condition was collected from the dynamic simulations of its full-scale finite element (FE) models mimicking field conditions. The FE model was validated using experimental and theoretical methods. The acceleration data in time domain were transformed into frequency domain using Fast Fourier Transform to determine peak amplitudes and their corresponding fundamental frequencies for the newest and the current condition of the bridge. The analyses and comparisons of the bridge dynamic response between the newest and the current bridge interestingly indicate a 37% reduction in its fundamental frequency over its 25 years of service life. This reduction has been correlated to the current condition rating of the bridge to develop application software for quick and efficient condition assessment of a PSBB bridge. The application software can instantly estimate overall bridge condition rating when used with the WSN deployed on a PSBB bridge under vehicular loads. The research outcome and the software is expected to provide a cost-effective solution for assessing the overall condition of a PSBB bridge, which helps to reduce maintenance costs and provide technologically improved bridge maintenance service.