scholarly journals Detecting damage events in concrete using diffuse ultrasound structural health monitoring during strong environmental variations

2017 ◽  
Vol 17 (2) ◽  
pp. 410-419 ◽  
Author(s):  
Patrik Fröjd ◽  
Peter Ulriksen
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Conditions ◽  
Continuous Wave ◽  
Data Set ◽  
Phase Measurements ◽  
Structural Health ◽  
Wave Measurements ◽  
Environmental Variations ◽  
Window Approach

Diffuse ultrasonic wave measurements used in structural health monitoring applications can detect damage in concrete. However, the accuracy is very susceptible to environmental variations. In this study, a large concrete floor slab was monitored using diffuse wave fields that were generated by continuous-wave transmissions between ultrasonic transducers. The slab was monitored for several weeks while being subjected to changes in environmental conditions. Subsequently, it was damaged using impact hits, resulting in centimeter-scale cracking. The variations caused by the environment masked the effects of the damage in the measurements. To address this issue, the Mahalanobis distance was used to distinguish between the influence of the damage and the influence of the environmental variations. The Mahalanobis model uses amplitude and phase measurements of continuous waves at a set of different frequencies as inputs. A moving window approach was applied to the baseline data set to account for slow trends. This study shows that this technique greatly suppresses most of the variations caused by environmental conditions. All damage events in our data set have been detected.

scholarly journals On sensor optimisation for structural health monitoring robust to environmental variations

2021 ◽  
Vol 6 (5) ◽  
pp. 1107-1116
Author(s):  
Tingna Wang ◽  
David J. Wagg ◽  
Keith Worden ◽  
Robert J. Barthorpe
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health State ◽  
Damage Classification ◽  
Health States ◽  
Structural Health ◽  
Classification Technique ◽  
Statistical Pattern ◽  
Environmental Variations ◽  
Using Data

Abstract. Structural health monitoring (SHM) is often approached from a statistical pattern recognition or machine learning perspective with the aim of inferring the health state of a structure using data derived from a network of sensors placed upon it. In this paper, two SHM sensor placement optimisation (SPO) strategies that offer robustness to environmental effects are developed and evaluated. The two strategies both involve constructing an objective function (OF) based upon an established damage classification technique and an optimisation of sensor locations using a genetic algorithm (GA). The key difference between the two strategies explored here is in whether any sources of benign variation are deemed to be observable or not. The relative performances of both strategies are demonstrated using experimental data gathered from a glider wing tested in an environmental chamber, with the structure tested in different health states across a series of controlled temperatures.

An On-Going Monitoring Project of a New Timber Structure

2013 ◽  
Vol 778 ◽  
pp. 757-764 ◽  
Author(s):  
Francesca Lanata
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Conditions ◽  
Environmental Changes ◽  
Construction Material ◽  
Structural Response ◽  
Timber Structures ◽  
The Real ◽  
Structural Health

Structural design, regardless of construction material, is based mainly on deterministic codes that partially take into account the real structural response under service and environmental conditions. This approach can lead to overdesigned (and expensive) structures. The differences between the designed and the real behaviors are usually due to service loads not taken into account during the design or simply to the natural degradation of materials properties with time. This is particularly true for wood, which is strongly influenced by service and environmental conditions. Structural Health Monitoring can improve the knowledge of timber structures under service conditions, provide information on material aging and follow the degradation of the overall building performance with time.A long-term monitoring control has been planned on a three-floor structure composed by wooden trusses and composite concrete-wood slabs. The structure is located in Nantes, France, and it is the new extension to the Wood Science and Technology Academy (ESB). The main purpose of the monitoring is to follow the long-term structural response from a mechanical and energetic point of view, particularly during the first few service years. Both static and dynamic behavior is being followed through strain gages and accelerometers. The measurements will be further put into relation with the environmental changes, temperature and humidity in particular, and with the operational charges with the aim to improve the comprehension of long-term performances of wooden structures under service. The goal is to propose new improved and optimized methods to make timber constructions more efficient compared to other construction materials (masonry, concrete, steel).The paper will mainly focus on the criteria used to design the architecture of the monitoring system, the parameters to measure and the sensors to install. The first analyses of the measurements will be presented at the conference to have a feedback on the performance of the installed sensors and to start to define a general protocol for the Structural Health Monitoring of such type of timber structures.

scholarly journals Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6894
Author(s):  
Nicola-Ann Stevens ◽  
Myra Lydon ◽  
Adele H. Marshall ◽  
Su Taylor
Keyword(s):  
Survival Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Transport Networks ◽  
Data Set ◽  
Structural Health

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

The Modal Identification of Structure Using Distributed ERA and EFDD Methods

2010 ◽  
Vol 163-167 ◽  
pp. 2532-2536
Author(s):  
Ying Lei ◽  
Zhi Lu Lai
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Volume ◽  
Modal Identification ◽  
Modal Parameters ◽  
Identification Algorithm ◽  
Civil Infrastructure ◽  
Complicated Structure ◽  
Data Set ◽  
Structural Health

Structural health monitoring (SHM) is an emerging field in civil engineering, offering the potential for continuous and periodic assessment of the safety and integrity of civil infrastructure. In this paper, a distributed computing strategy for modal identification of structure is proposed, which is suitable for the problem of solving large volume of data set in structural health monitoring. Numerical example of distribute computing the modal properties of truss illustrates the distributed out-put only modal identification algorithm based on NExT / ERA techniques and EFDD. This strategy can also be applied to other complicated structure to determine modal parameters.

Effects of environmental and operational variability on structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 539-560 ◽  
Author(s):  
Hoon Sohn
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Vibration Signal ◽  
Data Normalization ◽  
Operational Conditions ◽  
Structural Health ◽  
Structural Deterioration ◽  
Damage Data ◽  
Environmental Variations

Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.

Structural Health Monitoring under Varying Environmental Conditions Using Wavelets

2013 ◽  
Vol 569-570 ◽  
pp. 1218-1225
Author(s):  
Andreas Tjirkallis ◽  
Andreas Kyprianou ◽  
George Vessiaris
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Conditions ◽  
High Speed ◽  
Elevated Temperatures ◽  
Measurement Procedure ◽  
Full Field ◽  
Structural Health ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

A novel structural health monitoring system to detect damages in structures under varying operational and environmental conditions is presented in this paper. A noncontact, full-field measurement using a high speed camera offers a convenient and less expensive measurement procedure, enabling the measuring of responses in elevated temperatures and in conditions where contact sensors are unable to be used. In this paper, a combination of Decay lines of the Wavelet Transform Modulus Maxima (WTMM) and Holder Exponent (HE) are used to distinguish changes on the time response of a vibrating structure due to the operational and environmental variations to changes due to the presence of damage, thus minimising the possibility of false alarm. The proposed methodology is demonstrated using a 3-DOF system under conditions of varying and constant temperatures with the presence of damage, as well as using an experimental setup of a cantilever beam under intact and damaged conditions.

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