Challenges and potential of fiber optic sensors for structural health monitoring of bridges: a review

Structural health monitoring (SHM) has gained significant attention in the field of civil engineering due to effective maintenance of structures, particularly bridges. However, traditional SHM methods have limitations in providing accurate and continuous data, which has led researchers to explore new technologies, one of which is fiber optic sensor (FOS) monitoring. This paper provides a comprehensivereview of the use of FOS in bridge SHM, highlighting the challenges and potential of this technology. FOS are convenient for SHM due to their high accuracy, immunity toelectromagnetic interference, and capability of working in harsh environments. They are particularly suitable for quasi-distributive anddistributive measurement systems on capital civil engineering structures. FOS can be utilized to measurevarious parameters, including deformation, temperature, and strain. In bridge constructions, FOS can beinstalled in multiple locations. Deformation measurements using FOS can provide accurate information on the displacement and deflection of the bridge, which can help in detecting abnormalities or damages. Temperature measurements using FOS can detect effects of thermal load on bridges, which can cause significant damage. Strain measurements using FOS can help describe the stress distribution in the bridge, which can be used for maintenance purposes. FOS-based SHM systems can provide real-time and continuous data, which can help in detecting any potential problems at an early stage and preventing catastrophic failures. The use of FOS in SHM of bridges has been extensively researched and demonstrated in various studies. However, challenges such as installation, calibration, and interpretation of the data require further research. The paper will discuss the potential of FOS-based SHM systems in improving the safety and reliability of bridge constructions. It will also highlight the challenges related to FOS installation, calibration, and data interpretation and provide insights into future research directions for developing more robust and cost-effective FOS-based SHM systems

Review of Physical Based Monitoring Techniques for Condition Assessment of Corrosion in Reinforced Concrete

Monitoring the condition of steel corrosion in reinforced concrete (RC) is imperative for structural durability. In the past decades, many electrochemistry based techniques have been developed for monitoring steel corrosion. However, these electrochemistry techniques can only assess steel corrosion through monitoring the surrounding concrete medium. As alternative tools, some physical based techniques have been proposed for accurate condition assessment of steel corrosion through direct measurements on embedded steels. In this paper, some physical based monitoring techniques developed in the last decade for condition assessment of steel corrosion in RC are reviewed. In particular, techniques based on ultrasonic guided wave (UGW) and Fiber Bragg grating (FBG) are emphasized. UGW based technique is first reviewed, including important characters of UGW, corrosion monitoring mechanism and feature extraction, monitoring corrosion induced deboning, pitting, interface roughness, and influence factors. Subsequently, FBG for monitoring corrosion in RC is reviewed. The studies and application of the FBG based corrosion sensor developed by the authors are presented. Other physical techniques for monitoring corrosion in RC are also introduced. Finally, the challenges and future trends in the development of physical based monitoring techniques for condition assessment of steel corrosion in RC are put forward

Application of surrogate modeling methods in simulation-based reliability and performance assessment of civil structures

Structures and infrastructure systems are subjected to various deterioration processes due to environmental or mechanical stressors. Proper performance assessment approaches capable of detecting potential structural damage and quantifying the probability associated with structural failure are required to formulate optimal maintenance and retrofit plans that minimize the risk of failure and maximize the safety of structures. However, due to the presence of several sources of uncertainty that can affect the performance assessment and decision-making processes (e.g., uncertainties associated with loading conditions and performance prediction models), applying probabilistic methods, such as Monte Carlo simulation, is essential. In this context, a large number of simulations is generally required to quantify the low failure probability associated with civil structures. Executing the required number of simulations may be computationally expensive, especially if complex and/or nonlinear structural models (e.g., finite element models) are involved. The use of surrogate modeling tools such as artificial neural networks, polynomial chaos expansion, and kriging can help in reducing the computational costs associated with simulation-based probabilistic analysis. The research proposed herein aims to develop probabilistic approaches for performance assessment and damage detection of structures using advanced simulation-based techniques coupled with surrogate modeling. The proposed methodology is applied to quantify the risk of bridge failure due to flood events considering the impact of climate change. The approach was extended to establish the time-variant flood fragility surfaces for bridges under flood conditions. This approach (a) integrates deep learning neural networks into a simulation-based probabilistic approach to predict the future river streamflow necessary for assessing the flood hazard at the bridge location and (b) simulates the structural behavior of the bridge foundation under sour conditions. In addition, the proposed methodology is used to quantify the reliability of bolted and welded steel connections by integrating finite element analysis and surrogate models. Low-rank tensor approximation and polynomial chaos kriging surrogate models are adopted to perform Monte Carlo simulation and quantify the reliability of the investigated combination connection. Finally, artificial neural networks were used to develop a statistical damage detection and localization approach capable of evaluating the performance of prestressed concrete bridge girders using fiber optic sensors

Acoustic emission based structural health monitoring of corroded/un-corroded concrete structures

Structural health monitoring (SHM) is a continuous nondestructive evaluation system used for both damage prognosis and diagnosis of civil structures. Acoustic emission (AE) technique is defined as a passive SHM method that enables the detection of any possible damage. AE technique has been exploited for condition assessment and long-term monitoring of civil infrastructure systems. AE sensors are sensitive to the micro-cracking stage of damage, therefore showed a great potential for early detection of different forms of deteriorations in reinforced concrete (RC) structures. The rate of deterioration in RC structures greatly increases due to reinforcing steel corrosion embedded in concrete. Corrosion results in both expansion and mass loss of steel, thus causing concrete cover cracking and delamination. Moreover, corrosion causes reduction of bond between concrete and steel, which reduces the overall strength of RC structures. The objectives of this research were to: a) utilize AE monitoring for early corrosion detection and concrete cover/steel damage quantification of small-scale RC specimens, b) evaluate and localize corrosion activity using distributed AE sensors in full-scale RC beams, c) attain an early detection of loss of bond between corroded steel and concrete at different corrosion levels, d) identify and assess bond degradation of corroded/un-corroded bars in both small- and full-scale RC beams, and e) develop relationships between the collected AE data and variable levels of corrosion, corrosion-induced cover crack growth, and bond deterioration in RC structures. Four extensive experimental investigations have been conducted both on small- and fullscale RC elements to accomplish these aforementioned research objectives. AE monitoring was implemented in these studies on RC elements including a total of 30 prisms, 114 pull-out samples, and 10 beam anchorage specimens under accelerated corrosion, direct pull-out, and four-point load tests, respectively. The analysis of AE data obtained from these tests was performed and compared to the results of half-cell potentials (HCP) standard tests, visual detection of corrosion-induced cracks, crack width measurements, and overall bond behaviour of all tested pull-out samples/beams. The results showed that the analysis of AE signal parameters acquired during corrosion tests enabled the detection of both corrosion and cover crack onset earlier than HCP readings and prior to any visible cracking in both small- and full-scale RC beams, regardless of cover thickness or sensor location. Analyzing the AE signals attained from the pull-out tests permitted the characterization of two early stages of bond degradation (micro- and macro-cracking) in both corroded and un-corroded specimens at all values of bar diameter, corrosion level, cover thickness, and embedded length. The AE analysis also allowed an early identification of three stages of bond damage in full-scale corroded/uncorroded RC beams namely; first crack, initial slip, and anchorage cracking, before their visual observation, irrespective of corrosion level, embedded length, or sensor location. The results of AE intensity analysis on AE signal strength data were exploited to develop damage classification charts to assess the extent of corrosion damage as well as to categorize different stages of bond deterioration in corroded/un-corroded small- and fullscale RC samples

Novel Approaches for Structural Health Monitoring

The thirty-plus years of progress in the field of structural health monitoring (SHM) have left a paramount impact on our everyday lives. Be it for the monitoring of fixed- and rotary-wing aircrafts, for the preservation of the cultural and architectural heritage, or for the predictive maintenance of long-span bridges or wind farms, SHM has shaped the framework of many engineering fields. Given the current state of quantitative and principled methodologies, it is nowadays possible to rapidly and consistently evaluate the structural safety of industrial machines, modern concrete buildings, historical masonry complexes, etc., to test their capability and to serve their intended purpose. However, old unsolved problematics as well as new challenges exist. Furthermore, unprecedented conditions, such as stricter safety requirements and ageing civil infrastructure, pose new challenges for confrontation. Therefore, this Special Issue gathers the main contributions of academics and practitioners in civil, aerospace, and mechanical engineering to provide a common ground for structural health monitoring in dealing with old and new aspects of this ever-growing research field

Proceedings of the International RILEM Conference Materials, Systems and Structures in Civil Engineering segment on Service Life of Cement-Based Materials and Structures

Vol. 1O volume II encontra-se disponível em: http://hdl.handle.net/1822/4390

International RILEM Conference on Materials, Systems and Structures in Civil Engineering Conference segment on Service Life of Cement-Based Materials and Structures

Vol. 2O volume I encontra-se disponível em: http://hdl.handle.net/1822/4341