Expected utility theory for monitoring-based decision making

The main purpose of structural health monitoring (SHM) is to obtain information about the state of a structure, in order to guide bridge management decisions. Nevertheless, in practice, once a rigorous estimate of the structural state is available, decisions are usually made based on the decision maker’s intuition or experience. In this paper, we present the implementation of expected utility theory (EUT) in those civil engineering decision problems in which decision makers have to act based on the output of SHM. EUT is an analytical quantitative framework that allows the identification of the financially most convenient decisions, based on the possible outcomes of each action and on the probabilities of each structural state occurring. The advantage of the presented implementation is the optimization of decision strategies in SHM. In the manuscript, we first formalize the solution of single-stage decision processes, in which the decision maker has to take only one action. Then, we formalize the solution of multi-stage decision processes, in which multiple actions may be taken over time. Finally, using an example based on a case study, we describe the variables involved in the analysis of SHM decision problems, discuss the possible results and address the issues that may arise in the application of EUT in real-life settings

Evaluating the benefit of structural health monitoring for improving bridge resilience against scour. Deliverable D1 – Report on critical review of alternative techniques for bridge scour monitoring

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Substantial Decrease in Contaminant Concentrations in the Sediments of the Venice (Italy) Canal Network in the Last Two Decades—Implications for Sediment Management

The Venice canal network requires periodic intervention to remove sediments that progressively accumulate. The most recent dredging operation was carried out in the second half of the 1990s and early 2000s. These sediments had accumulated over a period of more than 30 years and were highly contaminated with Cd, Cu, Hg, Pb, Zn and PAHs. Sediments deposited after the dredging work were investigated in 2005, 2009, 2014 and 2017 by analysing sediment cores collected from three sites in the canal network. Arsenic, heavy metal and PAH concentrations were observed to be much lower than past values, although Cu, Hg and PAH levels were still relatively high. The high Cu concentrations (mean 161 mg kg−1) are partly due to the widespread use of Cu-based antifouling paint. Current Italian regulations forbid the disposal of dredged sediments with these concentrations inside the lagoon, thereby increasing the cost of canal network maintenance

A Procedure for Post-Earthquake Damage Estimation Based on Detection of High-Frequency Transients

In the current research structural health monitoring is considered for addressing the critical issue of post-earthquake damage detection. A non-standard approach for damage detection via acoustic emission is presented - acoustic emissions are monitored in the low frequency range (up to 120 Hz). Such emissions are termed high-frequency transients. Further a damage indicator defined as the Time-Ratio Damage Indicator is introduced. The indicator relies on time-instance measurements of damage initiation and deformation peaks. Based on the time-instance measurements a procedure for estimation of the maximum drift ratio is proposed. Monitoring data is used from a shaking-table test of a full-scale reinforced concrete bridge pier. Damage of the experimental column is successfully detected and the proposed damage indicator is calculated

Bayesian multi-parameter estimation using the mechanical equivalent of logical inference

In this work we illustrate how the mathematics of rational thinking is formally equivalent to that of structural mechanics. Concepts from the wold of logic, such as accuracy, uncertainty, Maximum a Posteriori (MAP) and rationality correspond, in the world of mechanics, to stiffness, flexibility, equilibrium and conservativeness. For instance, a linear Gaussian N-parameter estimation problem can be solved through a N-dof linear elastic system, as the analogy goes along these lines: the parameters covariance matrix is the system's flexibility matrixthe Fishers information is the stiffness matrixthe negative log-distribution of the parameters is the elastic potential energy of the systemthe Maximum a Posteriori (MAP) is the state of static equilibrium. In principle, based on this analogy, we could reproduce any logical inference problem with a finite element model, and make a judgment by finding its equilibrium state. We will show application of this analogy to a number of civil engineering inference problems, including Bayesian estimation, Bayesian networks and Kalman filter

Bayesian Approach to Condition Monitoring of PRC Bridges

This paper presents a damage detection procedure based on Bayesian analysis of data recorded by permanent monitoring systems as applied to condition assessment of Precast Reinforced Concrete (PRC) bridges. The concept is to assume a set of possible condition states of the structure, including an intact condition and various combinations of damage, such as failure of strands, cover spalling and cracking. Based on these states, a set of potential time response scenarios is evaluated first, each described by a vector of random parameters and by a theoretical model. Given the prior distribution of this vector, the method assigns posterior probability to each scenario as well as updated probability distributions to each parameter. The effectiveness of this method is illustrated as applied to a short span PRC bridge, which is currently in the design phase and will be instrumented with a number of fiber-optic long gauge-length strain sensors. A Finite Element Model is used to simulate the instantaneous and time-dependent behavior of the structure, while Monte Carlo simulations are performed to numerically evaluate the evidence functions necessary for implementation of the method. The ability of the method to recognize damage is discussed

Low power wireless sensor network for structural health monitoring of buildings using MEMS strain sensors and accelerometers

Within the MEMSCON project, a wireless sensor network was developed for structural health monitoring of buildings to assess earthquake damage. The sensor modules use custom-developed capacitive MEMS strain and 3D acceleration sensors and a low power readout application-specific integrated circuit (ASIC). A low power network architecture was implemented on top of an 802.15.4 media access control (MAC) layer in the 900MHz band. A custom patch antenna was designed in this frequency for optimal integration into the sensor modules. The strain sensor modules measure periodically or on-demand from the base station and obtain a battery lifetime of 12 years. The accelerometer modules record during an earthquake event, which is detected using a combination of the local acceleration data and remote triggering from the base station, based on the acceleration data from multiple sensors across the building. They obtain a battery lifetime of 2 years. The MEMS strain sensor and its readout ASIC were packaged in a custom package suitable for mounting onto a reinforcing bar inside the concrete and without constraining the moving parts of the MEMS strain sensor. The wireless modules, including battery and antenna, were packaged in a robust housing compatible with mounting in a building and accessible for maintenance such as battery replacement

Electromagnetic sensors for underwater scour monitoring

Scour jeopardises the safety of many civil engineering structures with foundations in riverbeds and it is the leading cause for the collapse of bridges worldwide. Current approaches for bridge scour risk management rely mainly on visual inspections, which provide unreliable estimates of scour and of its effects, also considering the difficulties in visually monitoring the riverbed erosion around submerged foundations. Thus, there is a need to introduce systems capable of continuously monitoring the evolution of scour at bridge foundations, even during extreme flood events. This paper illustrates the development and deployment of a scour monitoring system consisting of smart probes equipped with electromagnetic sensors. This is the first application of this type of sensing probes to a real case-study for continuous scour monitoring. Designed to observe changes in the permittivity of the medium around bridge foundations, the sensors allow for detection of scour depths and the assessment of whether the scour hole has been refilled. The monitoring system was installed on the A76 200 Bridge in New Cumnock (S-W Scotland) and has provided a continuous recording of the scour for nearly two years. The scour data registered after a peak flood event (validated against actual measurements of scour during a bridge inspection) show the potential of the technology in providing continuous scour measures, even during extreme flood events, thus avoiding the deployment of divers for underwater examination

Digital twin for civil engineering systems: an exploratory review for distributed sensing updating

We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects—its usefulness, modus operandi, application, etc.—and proves the suitability of Distributed Sensing as its network sensor component.This research was funded by Fondazione CARITRO Cassa di Risparmio di Trento e Rovereto, grant number 2021.0224.Peer ReviewedPostprint (published version