Design of Wireless Sensor Nodes for Structural Health Monitoring applications

Enabling low-cost distributed monitoring, wireless sensor networks represents an interesting solution for the implementation of structural health monitoring systems. This work deals with the design of wireless sensor networks for health monitoring of civil structures, specifically focusing on node design in relation to the requirements of different structural monitoring application classes. Design problems are analysed with specific reference to a large-scale experimental setup (the long-term structural monitoring of the Basilica S. Maria di Collemaggio, L’Aquila, Italy). Main limitations emerged are highlighted, and adopted solution strategies are outlined, both in the case of commercial sensing platform and of full custom solutions

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Proof of Concept of Wireless TERS Monitoring

Temporary earth retaining structures (TERS) help prevent collapse during construction excavation. To ensure that these structures are operating within design specifications, load forces on supports must be monitored. Current monitoring approaches are expensive, sparse, off-line, and thus difficult to integrate into predictive models. This work aims to show that wirelessly connected battery powered sensors are feasible, practical, and have similar accuracy to existing sensor systems. We present the design and validation of ReStructure, an end-to-end prototype wireless sensor network for collection, communication, and aggregation of strain data. ReStructure was validated through a six months deployment on a real-life excavation site with all but one node producing valid and accurate strain measurements at higher frequency than existing ones. These results and the lessons learnt provide the basis for future widespread wireless TERS monitoring that increase measurement density and integrate closely with predictive models to provide timely alerts of damage or potential failure

A wireless system for crack monitoring in concrete structures

The formation of cracks in concrete is a normal phenomenon. However, effective control and prevention of the formation of cracks is the key for successful life of concrete structures. Specifically, cracks represent a path of least resistance for moisture and corrosive ionic agents from de-icing salts to reach embedded steel in concrete. Commercial wireless sensor networks utilizing crack gauge sensors can be applied for crack monitoring in the common concrete structure. The crack sensors circuits\u27 boards, which are used to stimulate the cracks, are currently unavailable for the SG-Link module platform. The SG-Link module is an ultra-low-power module for use in sensor networks, monitoring applications and rapid application prototyping. Therefore, a crack sensor circuit board for the SG-Link module platform has been developed. The development of a smart wireless sensor network for the crack monitoring system is divided into four parts: a crack gauge sensor, signal conditioning, the SG-Link module, and a base station unit. The signal conditioning module consists of a crack gauge sensor, a wheatstone bridge, an amplifier, and a filter. The SG-Link module consists of an analog to digital converter (ADC), a microcontroller unit (MCC), and a transmitter with an antenna. The base station unit includes an antenna and a receiver module connected to the base station or computer. In this study, cracks are monitored based on the change of the electrical resistance between the sensor\u27s two terminals that are taken from the simulation model of the crack sensor board consisting of a crack gauge sensor and signal conditioning. This thesis looked at the effectiveness of a wireless system for crack monitoring in concrete structures. Tests were conducted in a laboratory to monitor the cracks in the structures and explore the validity and reliability of the monitoring mechanism and data transmission

Wireless sensor system for infrastructure health monitoring

In this thesis, radio frequency identification (RFID)-based wireless sensor system for infrastructure health monitoring (IHM) is designed and developed. It includes mountable semi-passive tag antenna integrated sensors capable of measuring critical responses of infrastructure such as dynamic acceleration and strain. Furthermore, the system is capable of measuring structural displacement. One of the most important parts of this system is the relatively small, tunable, construction material mountable RFID tag antenna. The tag antenna is electronically integrated with the sensors. Leading to the process of developing tag antenna integrated sensors having satisfactory wireless performance (sensitivity and read range) when mounted on concrete and metal structural members, the electromagnetic performance of the tag antenna is analyzed and optimized using both numerical and experimental procedures. Subsequently, it is shown that both the simulation and the experimental measurement results are in good agreement. The semi-passive RFID-based system is implemented in a wireless IHM system with multiple sensor points to measure dynamic acceleration and strain. The developed system can determine the natural frequencies of infrastructure and identify any state changes of infrastructure by measuring natural frequency shifts. Enhancement of the spectral bandwidth of the system has been performed under the constraints of the RFID hardware. The influence of the orientation and shape of the structural members on wireless power flow in the vicinity of those members is also investigated with the RFID reader-tag antenna system in both simulation and experiments. The antenna system simulations with a full-scale structural member have shown that both the orientation and the shape of the structural member influence the wireless power flow towards and in the vicinity of the member, respectively. The measurement results of the conducted laboratory experiments using the RFID antenna system in passive mode have shown good agreement with simulation results. Furthermore, the system’s ability to measure structural displacement is also investigated by conducting phase angle of arrival measurements. It is shown that the system in its passive mode is capable of measuring small structural displacements within a short wireless distance. The benchmarking of the developed system with independent, commercial, wired and wireless measurement systems has confirmed the ability of the RFID-based system to measure dynamic acceleration and strain. Furthermore, it has confirmed the system’s ability to determine the natural frequency of an infrastructure accurately. Therefore, the developed system with wireless sensors that do not consume battery power in data transmission and with the capability of dynamic response measurement is highly applicable in IHM

Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Permanent monitoring of thin structures with low-cost devices

Recently, structural monitoring technology invested in methodologies that give direct information on structures' stress state. Optic fibers, strain gauges, pressure cells give real-time data on the stress condition of a structural element, often determining the area where peak stresses have been reached, with a clear advantage over other less direct monitoring methodologies, such as, e.g., the use of accelerometers and inverse analysis to estimate internal forces. In addition, stresses can be recorded in a data log for analysis after a loading event, as well as for taking into account the lifelong stress state of the structure. Beams and columns of a reinforced concrete frame can be effectively monitored for flexural loads. Differently, thin shells are most of their lifespan under membrane regime, and, when properly designed, they rarely move to the bending regime. Our proposal is to monitor the stress in thin structures by small-sized low- cost devices able to record the stress history at key locations, sending alerts when necessary, with the aim of ensuring safety against the risk of collapse, or simply to perform maintenance/repairing activities. Such devices are realized with cheap off-the-shelf electronics and traditional strain gauges. The application examples are given as laboratory tests performed on a reinforced concrete plate, a masonry panel, and a steel beam. Results shows that the permanent monitoring control of stresses can be conveniently carried out on new structures using low-cost devices of the type we designed and realized in-house

Structural health monitoring of bridges using wireless sensor networks

Structural Health Monitoring, damage detection and localization of bridges using Wireless Sensor Networks (WSN) are studied in this thesis. The continuous monitoring of bridges to detect damage is a very useful tools for preventing unnecessary costly and emergent maintenance. The optimal design aims to maximize the lifetime of the system, the accuracy of the sensed data, and the system reliability, and to minimize the system cost and complexity Finite Element Analysis (FEA) is carried out using LUSAS Bridge Plus software to determine sensor locations and measurement types and effectively minimize the number of sensors, data for transmission, and volume of data for processing. In order to verify the computer simulation outputs and evaluate the proposed optimal design and algorithms, a WSN system mounted on a simple reinforced concrete frame model is employed in the lab. A series of tests are carried out on the reinforced concrete frame mounted on the shaking table in order to simulate the existing extreme loading condition. Experimental methods which are based on modal analysis under ambient vibrational excitation are often employed to detect structural damages of mechanical systems, many of such frequency domain methods as first step use a Fast Fourier Transform estimate of the Power Spectral Density (PSD) associated with the response of the system. In this study it is also shown that higher order statistical estimators such as Spectral Kurtosis (SK) and Sample to Model Ratio (SMR) may be successfully employed to more reliably discriminate the response of the system against the ambient noise and better identify and separate contributions from closely spaced individual modes. Subsequently, the identified modal parameters are used for damage detection and Structural Health Monitoring. To evaluate the preliminary results of the project\u27s prototype and quantify the current bridge response as well as demonstrate the ability of the SHM system to successfully perform on a bridge, the deployment of Wireless Sensor Networks in an existing highway bridge in Qatar is implemented. The proposed technique will eventually be applied to the new stadium that State of Qatar will build in preparation for the 2022 World Cup. This monitoring system will help permanently record the vibration levels reached in all substructures during each event to evaluate the actual health state of the stadiums. This offers the opportunity to detect potentially dangerous situations before they become critical

Studies on Spinal Fusion from Computational Modelling to ‘Smart’ Implants

Low back pain, the worldwide leading cause of disability, is commonly treated with lumbar interbody fusion surgery to address degeneration, instability, deformity, and trauma of the spine. Following fusion surgery, nearly 20% experience complications requiring reoperation while 1 in 3 do not experience a meaningful improvement in pain. Implant subsidence and pseudarthrosis in particular present a multifaceted challenge in the management of a patient’s painful symptoms. Given the diversity of fusion approaches, materials, and instrumentation, further inputs are required across the treatment spectrum to prevent and manage complications. This thesis comprises biomechanical studies on lumbar spinal fusion that provide new insights into spinal fusion surgery from preoperative planning to postoperative monitoring. A computational model, using the finite element method, is developed to quantify the biomechanical impact of temporal ossification on the spine, examining how the fusion mass stiffness affects loads on the implant and subsequent subsidence risk, while bony growth into the endplates affects load-distribution among the surrounding spinal structures. The computational modelling approach is extended to provide biomechanical inputs to surgical decisions regarding posterior fixation. Where a patient is not clinically pre-disposed to subsidence or pseudarthrosis, the results suggest unilateral fixation is a more economical choice than bilateral fixation to stabilise the joint. While finite element modelling can inform pre-surgical planning, effective postoperative monitoring currently remains a clinical challenge. Periodic radiological follow-up to assess bony fusion is subjective and unreliable. This thesis describes the development of a ‘smart’ interbody cage capable of taking direct measurements from the implant for monitoring fusion progression and complication risk. Biomechanical testing of the ‘smart’ implant demonstrated its ability to distinguish between graft and endplate stiffness states. The device is prepared for wireless actualisation by investigating sensor optimisation and telemetry. The results show that near-field communication is a feasible approach for wireless power and data transfer in this setting, notwithstanding further architectural optimisation required, while a combination of strain and pressure sensors will be more mechanically and clinically informative. Further work in computational modelling of the spine and ‘smart’ implants will enable personalised healthcare for low back pain, and the results presented in this thesis are a step in this direction