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Improved structural health monitoring of great belt bridge hangers and deck using digital image correlation
Published in Joan-Ramon Casas, Dan M. Frangopol, Jose Turmo, Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability, 2022
J. Winkler, F. Bormlund, M.D. Havelykke
Instruments such as strain gauges, accelerometers, fiber optic sensors and displacement transducers are becoming increasingly common in structural health monitoring. These types of sensors can however possess drawbacks such as the need for external power and cabling/antenna for data transmission, high data acquisition channel counts and the limitation of only measuring at discrete points or along a line, so it is necessary to have an idea of where to expect damage when placing the instrumentation. These sensors can be used effectively to continuously monitor for abnormalities that indicate damage, but the type and severity of the damage can still be difficult to identify from discrete point measurements. Furthermore, asset managers are continuously searching for new technologies that will allow them gathering information about their structures without traffic disruption.
Combination of GIS and SHM in Prognosis and Diagnosis of Bridges in Earthquake-Prone Locations
Published in Fadi Al-Turjman, Smart Grid in IoT-Enabled Spaces, 2020
Arman Malekloo, Ekin Ozer, Fadi Al-Turjman
SHM is a monitoring technology that can detect damage and inspect the overall performance of structures, ideally in real time and in a continuous manner (Chang, 1998). Coupling SHM with forecast system performance, also known as damage prognosis (DP), can enable behavioral predictions to estimate the useful renaming time of the structures under future loads (Farrar and Lieven, 2007). Typically, SHM systems consist of arrays of sensors deployed on strategic locations on bridges that can collect critical spatial information such as vibrations and displacement. As discussed earlier, the need for assessing multiple bridges on the network is essential to produce effective countermeasures; however, collocated inclusion of multiple bridge monitoring systems and their effect on transportation network will result in a considerable amount of data that is hard to capture, analyze, and manage. This is where GIS comes into the picture.
Structural health monitoring of submarine pressure hull using inverse finite element method
Published in J. Parunov, C. Guedes Soares, Trends in the Analysis and Design of Marine Structures, 2019
M.Y. Li, A. Kefal, B. Cerik, E. Oterkus
SHM is a multidisciplinary method by installing the sensors on the structure to obtain the required real-time data about the global/local health state of the structure aiming to detect the failure or damage of the structure in advance (Glisic & Inaudi, 2008). The detailed SHM process can be described as the following. First, sensors, mainly Fibre Optic Sensors (FBG) or strain gauges, are installed on the structures to collect the data which will then be transferred to a SHM system in real-time. Finally, the SHM will provide the information about the safety condition of the structure after analyzing the collected data (Gupta et al., 2004, Liu et al., 2018). Despite the available conventional sensing tools, FBG is preferred as a high technology sensor to collect strain data. This is mainly because as FBG sensors offer various advantages such as light weights, highly sensitive transmission within large structures etc. (Tian et al., 2015, Liu et al., 2015).
Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review
Published in Advanced Composite Materials, 2023
Muhammad Muzammil Azad, Sungjun Kim, Yu Bin Cheon, Heung Soo Kim
It is of significant importance to address the above-mentioned issues by continuously monitoring the condition of the composite structure. SHM can help in this regard by identifying any defects or damage in the early stages, allowing for timely maintenance and repair. This mitigates the risk of catastrophic failure, and extends the operational lifespan of composite structures. SHM uses the critical parameters from the composite structure through embedded sensors [20]. These parameters include strain, vibration, temperature, and wave signals that are capable of assessing the health state of composites [21]. The data provided by these sensory technologies also provide protection against operational and environmental conditions, consequently increasing the lifespan of the composite structure. The SHM approach is a cost-effective approach that can provide continuous data that can promptly reduce the causes of failure and the unnecessary maintenance operations of composite structures. Generally, the three main aspects of the health monitoring of composite structures are monitoring the load variation and environmental parameters, sensing the current state, and monitoring the structural damage.
Challenges in the application of digital transformation to inspection and maintenance of bridges
Published in Structure and Infrastructure Engineering, 2022
Marcos Massao Futai, Túlio N. Bittencourt, Hermes Carvalho, Duperron M. Ribeiro
The combination of modern inspection techniques, non-destructive testing and structural health monitoring allows more accurate diagnoses and prognoses of the structural behaviour of various types of structures, including bridges. The new technologies permit remote monitoring that can be combined with SHM in order to have an accurate analysis of the structure. Digital transformation is an inevitable process and will affect the competitiveness of various sectors of industry. Bridge inspection, monitoring, maintenance and management processes can benefit greatly from introducing new technologies and digital transformation in support of all the accumulated knowledge of the field in recent years. The Digital twin can be used to organize all the structural information necessary to compare the real time SHM data with to computational simulations. Based on these comparisons, prognoses of the structural behaviour can also be done with the support of the digital twin technology.
Machine Learning Based Quantitative Damage Monitoring of Composite Structure
Published in International Journal of Smart and Nano Materials, 2022
Xinlin Qing, Yunlai Liao, Yihan Wang, Binqiang Chen, Fanghong Zhang, Yishou Wang
Structural health monitoring (SHM) technology based on distributed sensor networks permanently integrated on the surface of or embedded inside composite structures is a revolutionary and innovative technology for determining the structural integrity of composite materials [5–12]. Through the built-in sensor network on the composite structure, the SHM technology obtains the information of the structure state and service environment in real time, so as to grasp the health status of structure in real time, and further predict the possible damage and failure, so that it can take timely action to ensure the safe use of the structure. SHM provides an important technical basis for establishing condition-based maintenance strategies based on the actual health and performance of structures to improve the safety and reduce the operation and maintenance costs. SHM can be generally divided into four levels: (1) determine whether the structure is damaged, (2) identify the location of damage, (3) assess the severity of damage, and (4) predict the remaining useful life of the structure. It can play an important role in the whole life cycle of composite structure, including design, manufacture, service and maintenance.