Structure health monitoring of housing project: A case study

<p>This paper introduces the methodology RWDI has developed, tested and consolidated over the years working in close collaboration with bridge designers, owners and operators, for the multi-hazard assessment of existing bridges and the ad hoc development of a structural health monitoring programme leading to enhanced resiliency. The work is highlighted through the presentation of a case study for a 2,725 m long cantilever bridge built in 1930. The dynamics of the structure in its current state were characterised and its capacity to today and future wind loading was assessed fully following the proposed methodology prior to the initiation of a structural rehabilitation program to extend the design life of the bridge beyond its 150th anniversary.</p>

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Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

Journal of Sensor and Actuator Networks ◽

10.3390/jsan7030030 ◽

2018 ◽

Vol 7 (3) ◽

pp. 30 ◽

Cited By ~ 35

Author(s):

Chiara Bedon ◽

Enrico Bergamo ◽

Matteo Izzi ◽

Salvatore Noè

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Full Scale ◽

Shaking Table ◽

Efficient Design ◽

Dynamic Identification ◽

Structural Health ◽

Cable Stayed Bridge ◽

Mems Accelerometers

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

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Structural reliability assessment based on optical monitoring system: case study

Revista IBRACON de Estruturas e Materiais ◽

10.1590/s1983-41952016000200009 ◽

2016 ◽

Vol 9 (2) ◽

pp. 297-305 ◽

Cited By ~ 2

Author(s):

E. Mesquita ◽

P. Antunes ◽

A. A. Henriques ◽

A. Arêde ◽

P. S. André ◽

...

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Structural Reliability ◽

Extreme Values ◽

Low Cost ◽

Reliability Assessment ◽

Water Reservoir ◽

Optical Systems ◽

Structural Health

ABSTRACT Optical systems are recognized to be an important tool for structural health monitoring, especially for real time safety assessment, due to simplified system configuration and low cost when compared to regular systems, namely electrical systems. This work aims to present a case study on structural health monitoring focused on reliability assessment and applying data collected by a simplified optical sensing system. This way, an elevated reinforced concrete water reservoir was instrumented with a bi-axial optical accelerometer and monitored since January 2014. Taking into account acceleration data, the natural frequencies and relative displacements were estimated. The reliability analysis was performed based on generalized extreme values distribution (GEV) and the results were employed to build a forecast of the reliability of the water elevated reservoir for the next 100 years. The results showed that the optical system combined with GEV analysis, implemented in this experimental work, can provide adequate data for structural reliability assessment.

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A cost effective three-axis accelerometer for building structure health monitoring

2017 IEEE Sensors Applications Symposium (SAS) ◽

10.1109/sas.2017.7894067 ◽

2017 ◽

Author(s):

Chih-Hsing Lin ◽

Chih-Wei Kang ◽

Chih-Chyau Yang ◽

Chien-Ming Wu ◽

Chun-Ming Huang

Keyword(s):

Health Monitoring ◽

Cost Effective ◽

Building Structure ◽

Structure Health Monitoring

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A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽

10.3390/s18092955 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2955 ◽

Cited By ~ 20

Author(s):

Mario de Oliveira ◽

Andre Monteiro ◽

Jozue Vieira Filho

Keyword(s):

Neural Network ◽

Structural Health Monitoring ◽

Convolutional Neural Network ◽

Lead Zirconate Titanate ◽

Health Monitoring ◽

Lead Zirconate ◽

Industrial Applications ◽

Electromechanical Impedance ◽

Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

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