While the objective of structural design is to achieve stability with an appropriate level of reliability, the design of systems for structural health monitoring is performed to identify a configuration that enables acquisition of data with an appropriate level of accuracy in order to understand the performance of a structure or its condition state. However, a rational standardized approach for monitoring system design is not fully available. Hence, when engineers design a monitoring system, their approach is often heuristic with performance evaluation based on experience, rather than on quantitative analysis. In this contribution, we propose a probabilistic model for the estimation of monitoring system effectiveness based on information available in prior condition, i.e. before acquiring empirical data. The presented model is developed considering the analogy between structural design and monitoring system design. We assume that the effectiveness can be evaluated based on the prediction of the posterior variance or covariance matrix of the state parameters, which we assume to be defined in a continuous space. Since the empirical measurements are not available in prior condition, the estimation of the posterior variance or covariance matrix is performed considering the measurements as a stochastic variable. Moreover, the model takes into account the effects of nuisance parameters, which are stochastic parameters that affect the observations but cannot be estimated using monitoring data. Finally, we present an application of the proposed model to a real structure. The results show how the model enables engineers to predict whether a sensor configuration satisfies the required performance.
This paper illustrates an application of Bayesian logic to monitoring data analysis and structural condition state
inference. The case study is a 260 m long cable-stayed bridge spanning the Adige River 10 km north of the town of
Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay
cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that
long-term load redistribution between cables can be expected. To monitor load redistribution, the owner decided to
install a monitoring system which combines built-on-site elasto-magnetic and fiber-optic sensors. In this note, we discuss
a rational way to improve the accuracy of the load estimate from the EM sensors taking advantage of the FOS
information. More specifically, we use a multi-sensor Bayesian data fusion approach which combines the information
from the two sensing systems with the prior knowledge, including design information and the outcomes of laboratory
calibration. Using the data acquired to date, we demonstrate that combining the two measurements allows a more
accurate estimate of the cable load, to better than 50 kN.
This paper illustrates an application of Bayesian logic to monitoring data analysis and structural condition state inference. The case study is a 260 m long cable-stayed bridge spanning the Adige River 10 km north of the town of Trento, Italy. This is a statically indeterminate structure, having a composite steel-concrete deck, supported by 12 stay cables. Structural redundancy, possible relaxation losses and an as-built condition differing from design, suggest that long-term load redistribution between cables can be expected. To monitor load redistribution, the owner decided to install a monitoring system which combines built-on-site elasto-magnetic and fiber-optic sensors. In this note, we discuss a rational way to improve the accuracy of the load estimate from the EM sensors taking advantage of the FOS information. More specifically, we use a multi-sensor Bayesian data fusion approach which combines the information from the two sensing systems with the prior knowledge, including design information and the outcomes of laboratory calibration. Using the data acquired to date, we demonstrate that combining the two measurements allows a more accurate estimate of the cable load, to better than 50 kN.
The motivation of this work is the installation of a monitoring system on a new cable-stayed bridge spanning the Adige
River 10 km north of the town of Trento. This is a statically indeterminate structure, having a composite steel-concrete
deck of length 260 m overall, supported by 12 stay cables, 6 per deck side. These are full locked steel cables of diameters
116 mm and 128 mm, designed for operational loads varying from 5000 to 8000 kN. The structural redundancy suggests
that plastic load redistribution among the cables can be expected in the long term. To monitor such load redistribution,
the owner decided to install a monitoring system to measure cable stress; the precision specified was of the order of few
MPa. However no cable release or any form of on-site calibration involving tension change was allowed. The solution
found was a combination of built-on-site electromagnetic and fiber-optic elongation gauges, these appropriately
distributed on both the cables and the anchorages. We discuss how the set of gauges allows tension and elongation
measurement with the appropriate precision, and compare the initial monitoring results with the tension estimates made
using a non-destructive vibration test.
In the field of Structural Health Monitoring, tests and sensing systems are intended as tools providing diagnoses, which
allow the operator of the facility to develop an efficient maintenance plan or to require extraordinary measures on a
structure. The effectiveness of these systems depends directly on their capability to guide towards the most optimal
decision for the prevailing circumstances, avoiding mistakes and wastes of resources. Though this is well known, most
studies only address the accuracy of the information gained from sensors without discussing economic criteria. Other
studies evaluate these criteria separately, with only marginal or heuristic connection with the outcomes of the monitoring
system. The concept of "Value of Information" (VoI) provides a rational basis to rank measuring systems according to a
utility-based metric, which fully includes the decision-making process affected by the monitoring campaign. This
framework allows, for example, an explicit assessment of the economical justifiability of adopting a sensor depending on
its precision.
In this paper we outline the framework for assessing the VoI, as applicable to the ranking of competitive measuring
systems. We present the basic concepts involved, highlight issues related to monitoring of civil structures, address the
problem of non-linearity of the cost-to-utility mapping, and introduce an approximate Monte Carlo approach suitable for
the implementation of time-consuming predictive models.
The evaluation of seismic damage is today almost exclusively based on visual inspection, as building owners are
generally reluctant to install permanent sensing systems, due to their high installation, management and maintenance
costs. To overcome this limitation, the EU-funded MEMSCON project aims to produce small size sensing nodes for
measurement of strain and acceleration, integrating Micro-Electro-Mechanical Systems (MEMS) based sensors and
Radio Frequency Identification (RFID) tags in a single package that will be attached to reinforced concrete buildings. To
reduce the impact of installation and management, data will be transmitted to a remote base station using a wireless
interface. During the project, sensor prototypes were produced by assembling pre-existing components and by
developing ex-novo miniature devices with ultra-low power consumption and sensing performance beyond that offered
by sensors available on the market. The paper outlines the device operating principles, production scheme and working
at both unit and network levels. It also reports on validation campaigns conducted in the laboratory to assess system
performance. Accelerometer sensors were tested on a reduced scale metal frame mounted on a shaking table, back to
back with reference devices, while strain sensors were embedded in both reduced and full-scale reinforced concrete
specimens undergoing increasing deformation cycles up to extensive damage and collapse. The paper assesses the
economical sustainability and performance of the sensors developed for the project and discusses their applicability to
long-term seismic monitoring.
Motivated by the preservation of an artistic treasure, the fresco of the "Cycle of the Months" on the second floor in an
historic tower, Torre Aquila, a wireless sensor network (WSN) has been developed and installed for permanent health
monitoring. The monitoring scheme covers both static and dynamic evaluation of the tower structural integrity from local
to global scale and consists of 17 nodes, including 2 long length fiber optic sensors (FOS), 3 accelerometers and 12
environmental nodes. The system has been working for 1.5 years and has been debugged and updated both as to
hardware and software. This paper focuses mainly on the ambient vibration analysis used to investigate the performance
of the sensor nodes and structural properties of the tower. Initial ambient vibration monitoring shows that cyclic
environmental factors, such as traffic flow, are not the dominant cause of tower vibration; and the vibration levels of the
tower in different axes are not large enough to be a critical issue calling for attention under current conditions. It proves
that the WSN is an effective tool, capable of providing information relevant to safety assessment of the tower.
Residuals that capture the difference between anticipated behavior and actual observations are often used to identify
damage. Wanting to control the influence of unmeasured disturbances and noise in residuals, it is common to generate
reference signals using feedback from measured outputs. Since there is much flexibility in the gains a wide range of
models that react differently to changes are possible. This paper examines two questions: 1) how damage residuals
generated by different closed loop models relate to each other and 2) how to rank the expected efficiency of alternative
models. On the first question examination shows that the residuals from any model can be viewed as sums of filtered
open loop residuals where the filter coefficients depend on the model structure but not on the damage. On the second
item a general procedure based on Bayesian decision-making is proposed to quantify the economical benefit in adopting
a specific autoregressive model.
This paper introduces the concept and development of a strain sensing system for structural application based on the
properties of photonic crystals. Photonic crystals are artificially created periodic structures, usually produced in the thinfilm
form, where optical properties are tailored by a periodicity in the refractive index. The idea of using the crystal as a
sensor is based on the observation that a distortion in the crystal structure produces a change in the reflected bandwidth.
When a photonic crystal is designed to operate in the visible part of the spectrum, a permanent distortion of the film
results in a change in its apparent color. This property makes photonic crystals suitable for permanent monitoring of
structural elements, as any critical changes in the strain field can be promptly and easily detected by visual inspection. A
simple and low-cost example of photonic crystals consists of opals synthesized by vertical deposition. In this
contribution we introduce a target application for the fatigue monitoring of wind turbines, and then provide the reader
with some basic information concerning modeling of the crystal architecture and fabrication of these structures. Next we
discuss their application to strain measurement, specifying how reflection and transmission properties of the opals have
to be designed to satisfy the expected strain response of the sensor. Finally, we present the preliminary results of a
laboratory validation carried out on thin films applied to a rubber support.
This paper introduces a concept of smart structural elements for the real-time condition monitoring of bridges. These are
prefabricated reinforced concrete elements embedding a permanent sensing system and capable of self-diagnosis when in
operation. The real-time assessment is automatically controlled by a numerical algorithm founded on Bayesian logic: the
method assigns a probability to each possible damage scenario, and estimates the statistical distribution of the damage
parameters involved (such as location and extent). To verify the effectiveness of the technology, we produced and tested
in the laboratory a reduced-scale smart beam prototype. The specimen is 3.8 m long and has cross-section 0.3 by 0.5m,
and has been prestressed using a Dywidag bar, in such a way as to control the preload level. The sensor system includes
a multiplexed version of SOFO interferometric sensors mounted on a composite bar, along with a number of traditional
metal-foil strain gauges. The method allowed clear recognition of increasing fault states, simulated on the beam by
gradually reducing the prestress level.
One of the advantages of fiber optics with respect to traditional electrical gauges is they can act both as sensors and as a
pathway for signals produced by other sensors. This feature allows adoption of a simple sensor system architecture, even
when arrays of many sensors are needed. In this paper, we present the development and laboratory validation of in-line
multiplexing for the low-coherence interferometric SOFO standard deformation sensor. A standard SOFO, as developed,
produced and commercialized by Smartec SA, employs total reflectors at the end of the measurement and the reference
fibers, allowing measurement of the strain over a single field. In the solution presented, broadband Fiber Bragg Gratings
(FBGs) are employed as partial reflectors to obtain in-line multiplexing. These FBGs, presenting a 5% reflectivity in
their reflection spectra, are produced using a chirped phase mask. An experiment was carried out on a 3-field sensor, to
investigate effectiveness, resolution and temperature sensitivity. Outcomes show clear fringe visibility for each pair of
gratings and a resolution of about 2.5 &mgr;m RMS, of the same order as the single field sensor. Issues regarding the
maximum number of measurement fields on a single line and deformation range limits are discussed.
Recognizing the growing importance of new technologies in the life-cycle management of civil infrastructure, the University of Trento is promoting a research effort aimed at developing a novel construction system that will allow real-time condition monitoring of bridge structures. The general concept is to build new bridges using smart structural elements, i.e. precast RC elements embedding a sensing system and capable of self-diagnosis. Sensors are conceived as an integral part of the prefabricated element, influencing its design criteria, performance and detailing. A first step of the research was accomplished in 2004 with the construction and testing of reduced-scale prototypes of smart elements. The second phase aims at demonstrating the industrial feasibility of the series production of prefabricated elements embedding FOS technology as well as their in-field reliability. In detail, the program includes the production of two 28m-long prestressed RC box-beam elements. One of these will be used in a single span road bridge, while the other will be extensively tested in the laboratory, in order to record and identify the response signature associated with recurrent deterioration scenarios. The general paradigm of the design is to conceive the sensing system in two separate parts, embeddable and external. The embeddable part is to be permanently installed in the element, and therefore must have high durability and robustness, while the external sensing system can be replaced during routine maintenance work or as necessary in the case of malfunction, or for technology upgrade.
A research effort has been launched at the University of Trento, aimed at developing an innovative distributed construction system based on smart prefabricated concrete elements allowing for real-time condition assessment of civil infrastructures. So far, two reduced-scale prototypes have been produced, each consisting of a 0.2 by 0.3 by 5.6m RC beam specifically designed for permanent instrumentation with 8 long-gauge Fiber Optics Sensors (FOS) at the lower edge. The sensors employed are Fiber Bragg Grating (FBG) -based and can measure finite displacements both in statics and dynamics. The acquisition module uses a single commercial interrogation unit and a software-controlled optical switch, allowing acquisition of dynamic multi-channel signals from FBG-FOS, with a sample frequency of 625 Hz per channel. The performance of the system underwent validation I n the laboratory. The scope of the experiment was to correlate changes in the dynamic response of the beams with different damage scenarios, using a direct modal strain approach. Each specimen was dynamically characterized in the undamaged state and in various damage conditions, simulating different cracking levels and recurrent deterioration scenarios, including concrete cover spalling and partial corrosion of the reinforcement. The location and the extent of damage are evaluated by calculating damage indices which take account of changes in frequency and in strain-mode-shapes. This paper presents in detail the results of the experiment and demonstrates how the damage distribution detected by the system is fully compatible with the damage extent appraised by inspection.
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