# Monitoring-Supported Value Generation for Managing Structures and
Infrastructure Systems

<EMAIL_ADDRESS>Eleni Chatzi  Daniel Straub  Nikolaos
Dervilis  Kai Goebel  Aidan J. Hughes  Geert Lombaert  Costas Papadimitriou
Konstantinos G. Papakonstantinou  Matteo Pozzi  Michael Todd  Keith Worden
Institute of Structural Engineering, ETH Zurich, Stefano-Franscini-Platz 5,
8093 Zurich, Switzerland  Engineering Risk Analysis Group & Munich Data
Science Institute, Technical University of Munich, Arcisstr. 21, 80333 Munich,
Germany  Dynamics Research Group, Department of Mechanical Engineering,
University of Sheffield,Sheffield, S1 3JD, UK  SRI International/PARC,Palo
Alto, CA 94304, USA  Department of Civil Engineering, KU Leuven, Leuven,
Belgium  Department of Mechanical Engineering, University of Thessaly, Pedion
Areos, 38334, Greece  Department of Civil & Environmental Engineering, The
Pennsylvania State University, University Park, PA, USA  Civil and
Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA

###### Abstract

To maximize its value, the design, development and implementation of
Structural Health Monitoring (SHM) should focus on its role in facilitating
decision support. In this position paper, we offer perspectives on the synergy
between SHM and decision-making. We propose a classification of SHM use cases
aligning with various dimensions that are closely linked to the respective
decision contexts. The types of decisions that have to be supported by the SHM
system within these settings are discussed along with the corresponding
challenges. We provide an overview of different classes of models that are
required for integrating SHM in the decision-making process to support
management and operation and maintenance of structures and infrastructure
systems. Fundamental decision-theoretic principles and state-of-the-art
methods for optimizing maintenance and operational decision-making under
uncertainty are briefly discussed. Finally, we offer a viewpoint on the
appropriate course of action for quantifying, validating and maximizing the
added value generated by SHM. This work aspires to synthesize the different
perspectives of the SHM, Prognostic Health Management (PHM), and reliability
communities, and deliver a roadmap towards monitoring-based decision support.

###### keywords:

SHM; decision support; maintenance planning; value of information; population-
based SHM; verification & validation

††articletype: POSITION PAPER††journal: Data-Centric Engineering

2024

[1]Antonios Kamariotis

A. Kamariotis et al.

[Impact Statement] Structural Health Monitoring (SHM) systems can be viewed as
decision-support tools. This position paper aims to deliver a roadmap towards
monitoring-supported value creation and systematic integration of SHM in the
operation and maintenance decision-making process for structures and
infrastructure systems.

##  1 Introduction

Structural Health Monitoring (SHM) offers a potent tool to enhance the
Operation & Maintenance (O&M) decision-making process for structures and
infrastructure systems (Farrar and Worden,, 2013). SHM systems can essentially
be viewed as a collection of tools for decision support (Hughes et al.,, 2021;
Kamariotis et al., 2023a, ). Yet, to date, SHM systems have not been broadly
deployed on real-world structures and infrastructure systems (Cawley,, 2018).
This is not least because of the fact that the decision-support potential of
SHM remains relatively unexplored. SHM research has been mainly driven by
technological and methodological advancements without explicitly taking into
consideration insights and methods from the risk/reliability and decision-
making communities. Few recent efforts have been made to connect these two
lines of research and formally explore the Value of Information (VoI) stemming
from SHM (Pozzi and Kiureghian,, 2011; Thöns,, 2018; Hughes et al.,, 2021;
Kamariotis et al., 2023a, ). In the experience of the authors, different
research fields comprise different perspectives, often entailing distinct
vocabularies, which renders exchange challenging. Moreover, owners and
operators of structures and infrastructure systems and SHM practitioners must
be convinced in order to adopt advanced SHM technologies and trust decision
algorithms for assisting them in the O&M process. A paradigm shift is
required, as typically the O&M process heavily depends on a rule-based
philosophy and is strongly regularized. Actionable use cases are required for
illustrating the manner in which SHM systems can support different decision
settings, thereby generating a return on investment.

In this position paper, experts from the SHM, risk/reliability and decision-
making, as well as the Prognostic Health Management (PHM) communities, jointly
offer perspectives on the synergistic development of SHM and decision-making
tools, and discuss multiple associated challenges. Specifically, in Section 2
we propose a classification of SHM use cases across different dimensions that
feed the O&M decision-making process. Section 3 overviews both purely data-
driven and hybrid diagnostic/prognostic models and discusses how these
facilitate a monitoring-informed maintenance planning process. Section 4
discusses the optimization of maintenance planning strategies under
availability of monitoring data, and offers an overview of the Value of
Information (VoI) framework. In Section 5, we discuss approaches and
directions towards increased value generation with SHM. Finally, Section 6
offers brief concluding remarks.

##  2 SHM use cases in relation to the decision-making process

SHM finds application in various use cases, each associated with different
contexts in which decisions can be supported by the monitoring data and SHM
processing algorithms. We here discuss some main dimensions along which SHM
use cases can be classified, with each dimension describing different aspects
that influence the O&M decision-making task. These dimensions are described in
Sections 2.1, 2.2 and 2.3 and are illustrated in Fig. 1. For each dimension,
we further discuss specific decision settings and associated challenges.

Figure 1: SHM use cases across dimensions that influence decision-making for
monitored structures

###  2.1 Monitoring across temporal scales

Kamariotis et al., 2023a  present a classification of SHM use cases in terms
of the associated time scales for decision-making, ranging from real-time
needs (sub-second accuracy) to decisions spanning the lifetime of the system.
At the lower end of the scale (seconds to hours), SHM can be deployed to
deliver real-time or near real-time diagnostics. The assessment goal at this
time scale is the fast, almost online, detection of flaws or abnormalities.
Examples include the real-time detection of a sudden fault in a control
system, or the flagging of structures after an earthquake event (Tubaldi et
al.,, 2022). At the scale of days to months, an objective of SHM is to
identify fast-evolving structural deterioration processes that could
compromise serviceability or safety, such as, e.g., freeze-thaw or akali-
silica reaction processes. Furthermore, at this scale, SHM can serve for post-
event assessment following extreme events (e.g., floods) or devising
appropriate remedial strategies. Finally, at the scale of larger time spans
(years to lifetime), SHM can be leveraged to support condition-based or
predictive maintenance decisions associated with slow-evolving deterioration
processes. Assessment is effectuated via use of both purely data-driven or
hybrid schemes, where either data exclusively or data coupled with physics-
based models are used to form twinning, diagnostic and prognostic tools (see
Section 3). Such assessment is typically impaired by presence of confounding
processes, typically reflected in Environmental and Operational Variability
(EOV) (see Fig. 1) (Peeters et al.,, 2001; Figueiredo et al.,, 2011; Cross et
al.,, 2011). The time scale determines the urgency of the decision-making
task, which consequently dictates the type of models and decision-making
strategies that can be employed, as discussed in detail in Sections 3 and 4
respectively.

Currently, SHM systems are most typically called upon to assist in closer
examination of existing systems that exhibit identified potential problems, or
to support decisions related to lifetime extension, with sensors deployed at
an advanced stage of the structural life-cycle. However, SHM can also be
integrated during the design phase, escorting structural systems from cradle
to grave (Farrar and Worden,, 2013; Hulse et al.,, 2020). Depending on the
stage in which installation or extension of a monitoring deployment is
contemplated, Value of Information (VoI) or Value of SHM (VoSHM) analyses (see
Section 4) can inform the decision on whether or not to install a specific SHM
system on a target structure, as well as advise on the configuration of the
monitoring deployment for maximizing the associated VoI, according to the
objective at hand.

###  2.2 Monitoring for varying structural performance requirements

Opportunities and challenges for SHM-supported decision making depend on the
criticality of the monitored processes. In structural engineering, it is
common to distinguish between serviceability requirements, which relate to
ensuring the intended use of the structure, and safety requirements, which
relate to ensuring the safe operation of the system. The latter are typically
much stricter and require compliance with regulations and codes, which are
often rule-based. It can be difficult for owners and operators to deviate from
these rules, and in these cases SHM cannot reduce the cost of prescribed
inspections and maintenance, unless it is also taken into account in forming
new guidelines. In some application areas, e.g., in earthquake engineering,
performance-based requirements are increasingly considered, but these are
still the exception rather than the norm. By contrast, regulations for
serviceability requirements are typically less strict and owners of structures
have some latitude on how to to ensure serviceability. Hence it can be easier
currently to include SHM into the decision process when dealing with
servicability issues. This differentiation between serviceability and safety
issues can also affect the type and urgency of associated maintenance actions.

###  2.3 SHM for individual structures versus population-based SHM

SHM use cases depend also on the scale of the monitored object(s) and the
associated decision making, which can be at the component, individual asset,
and eventually the population (fleet) level. A population-based approach to
SHM has recently been introduced in a series of contributions (Bull et al.,,
2021; Gosliga et al.,, 2021; Gardner et al.,, 2021; Tsialiamanis et al.,
2021a, ). Population-based SHM (PBSHM) is characterised by the sharing of
information between sufficiently-similar structures, with the aim of improving
predictive performance and decision-making. PBSHM can mitigate the problem of
data scarcity for individual structures, which prevents full exploitation of
supervised learning in data-driven approaches. Population-based approaches to
SHM extend the use cases of monitoring systems to supporting O&M decision-
making for fleets of structures – this extension is reflected as a third
dimension in Fig. 1. Another consideration for population-based SHM is that
the deployment of full-scale monitoring systems for all structures within a
population may be too costly, or that diminishing returns are seen in terms of
VoSHM as more members of the population are subject to full-scale monitoring.
Therefore, it may be preferable to target a few salient structures with full-
scale SHM systems, and rely on reduced-scale monitoring in conjunction with
transfer learning (Pan and Yang,, 2010) (or other information-sharing
technologies) in order to support decision-making for the remaining structures
in the population. Finally, it is worth noting that the value of PBSHM
includes a component associated with the expected utility gained as a result
of sharing, or transferring, information between structures. This quantity,
termed the (expected) value of information _transfer_ , is useful to consider
as it can be used to select optimal algorithms and parameters to conduct
transfer learning (Hughes et al.,, 2024).

###  2.4 Industry and regional culture

The industrial culture is an important aspect to consider. In many industries,
a rule-based methodology is typically followed in the practical O&M process.
For instance, for bridge structures, inspections and maintenance actions are
typically scheduled based on a fixed period (e.g., inspections every 2-3
years), as required by codes and standards (Ryan et al.,, 2022). Adoption of
SHM for enhancing the O&M process requires transition to a performance-based
methodology; this is followed to a certain extent in O&M of wind turbines
(GE,, 2017). Furthermore, there exist different degrees of regularization in
terms of maintenance requirements and practices across different countries and
regions, which has an impact on the feasibility of a paradigm shift regarding
the O&M process.

##  3 Model classes for SHM-based assessment

This section describes the different model classes that are required for
integrating SHM in the O&M decision-making process, as illustrated in Fig. 2.
One can distinguish between at least two distinct maintenance planning
paradigms that are enabled by SHM: i) condition-based maintenance (CBM) and
ii) predictive maintenance (PdM) planning (Fink,, 2020; Goebel et al.,, 2017).
Following a CBM strategy, maintenance is informed at the moment when a
threshold is exceeded, imposed either directly on the value of an observation
or on the value of a damage indicator, which relates to the current condition
of the system. Instead, a PdM strategy relies on prognostic models, developed
also through SHM and related data, which deliver predictions of the future
evolution of a system’s condition, with maintenance informed on the basis of
these predictions. While a CBM-based approach is accomplished on the basis of
availability of data and the frequent accompanying use of related models, a
PdM-based approach often requires the inclusion of a physics- or engineering-
based model in the loop. This holds particularly in instances where the
systems being monitored lack sufficient experimental data to failure. While
such data may be available for certain standardized (non-unique) engineering
systems (e.g., in industrial engineering) (Zio,, 2022), they are typically not
available for safety-critical civil and infrastructure engineering systems,
which generally feature more individual designs.

When no models are available a-priori, SHM data alone can be utilized for the
training of purely data-driven models, also referred to as black-box models,
for damage diagnosis/prognosis. Purely data-driven models can be inferred via
the use of system identification (Söderström and Stoica,, 1988) and/or machine
learning (ML) schemes (Farrar and Worden,, 2013; Malekloo et al.,, 2022).
Data-driven models cannot easily move away from existing experience and thus
typically fail to extrapolate to future predictions regarding the evolution of
damage, i.e., they fail to extend from damage diagnosis to prognosis (Farrar
and Worden,, 2013). The effective development of purely data-driven prognostic
models relies on datasets that contain monitoring data corresponding to damage
states of several systems similar to the system of interest (NASA,, 2023),
which poses a challenge for structures, where designs are individualized.
PBSHM attempts to tackle this challenge by capitalizing on partial
similarities of such structures for generalizing models and transferring the
knowledge gained from monitoring several such individual instances
(Tsialiamanis et al.,, 2023). To date, the purely data-driven PdM planning
paradigm has enjoyed broader application within the Prognostic Health
Management (PHM) discipline (Nguyen and Medjaher,, 2019; Fink,, 2020; Lee and
Mitici,, 2023; Kamariotis et al.,, 2024), with its application to structures
and infrastructure systems remaining scarce.

When physics-based models are available (e.g., through finite element (FE)
models), these can offer valuable intuition into the underlying system. In
this case, SHM data can be fused with physics-based models, resulting in
hybrid or grey-box representations, which can serve diagnostic, prognostic,
and eventually, decision support tasks (Arias Chao et al.,, 2022; Liu et al.,,
2022; Cross et al.,, 2023). A hybrid model can refer to i) estimators of
quantities of interest that incorporate physics principles (e.g., physics-
informed Gaussian Processes (Cross et al.,, 2023)) or ii) full-blown Digital
Twins (DTs) (Wagg et al.,, 2020; Chinesta et al.,, 2020; VanDerHorn and
Mahadevan,, 2021; Thelen et al.,, 2022). When a DT is constructed offline, via
a one-off model updating process (Simoen et al.,, 2015), then this can be
viewed as a Digital Twin Instance (DTI) (McClellan et al.,, 2022). When such
an updating process is executed continually, this can be viewed as a closed
loop DT, also referred to as DT aggregate. It reflects an aggregation of DTIs,
which allows tracking the structure (physical twin) throughout its life-cycle
and informing decisions that realize value (AIAA,, 2022). In the particular
case where data and models can be fused on the fly, as data are attained, we
refer to a Real-Time Digital Twin (RTDT) (Vettori et al.,, 2023). Referring to
the prior categorization of SHM use cases across time scales, it becomes
evident that detailed engineering models can typically not be harnessed for
real-time or near real-time tasks. In tackling this challenge, Reduced Order
Models (ROMs) (Benner et al.,, 2015; Chinesta et al.,, 2016; Vlachas et al.,,
2021), or surrogate representations (Lüthen et al.,, 2021), form invaluable
tools that reduce the computational complexity of detailed engineering models
and allow for real-time tasks to be accomplished.

Figure 2: Modeling layers required for SHM-aided operation and maintenance
planning

When considering the environment/context within which a system operates, a
hybrid setting requires prior knowledge on the types of damage and the
mathematical definition of deterioration processes acting on the monitored
structure or infrastructure. This knowledge is typically embedded in the
a-priori definition of empirical or physics-based deterioration models (van
Noortwijk,, 2009; Jia and Gardoni,, 2018). However, adequate quantitative
deterioration models exist only for a small subset of deterioration phenomena
acting on structures and infrastructures (Straub,, 2018). This issue poses
significant challenges, as such models are indispensable for making
predictions. A strong need therefore exists for the development of improved
deterioration models, a process that can be assisted by the existence of
monitoring data. If such models are available, they can be updated on-line, or
even partially inferred, based on the monitoring data, thus delivering data-
informed predictions of the damage evolution (Straub,, 2009; Zio and Peloni,,
2011; Corbetta et al.,, 2018; Kamariotis et al., 2023b, ; Morato et al.,,
2023). This assimilation of deterioration models and SHM data essentially also
forms part of hybrid modeling. In this context, a PdM strategy can be employed
based on input from hybrid prognostics, which rely on availability of a
physics-based model of the engineering system and a deterioration model, and
their fusion with SHM data.

##  4 Decision-making under uncertainty with SHM

SHM-aided maintenance planning is a problem of decision-making under
uncertainty. It can be performed following a CBM or a PdM decision strategy. A
decision strategy $S$ consists of a set of decision rules adopted at all time
steps of a sequential decision problem, specifying the action(s) to take
(among a predefined set of actions) at each time step.

A decision strategy may be assigned a-priori, i.e., a decision maker may
suboptimally assign a threshold on the value of a damage diagnostic/prognostic
indicator, which, when exceeded, will inform a maintenance action. Fixing a
decision strategy a-priori requires prior engineering expertise regarding the
considered system. This approach best reflects what is typically done in
practice, where a formal strategy optimization is rarely performed.

In the case of systems for which monitoring data from damage states from
similar systems exist, the a-priori definition of a decision strategy is
certainly assisted by the existence of such data. Furthermore, when prior
information is available, and the systems are standardized (e.g., wind
turbines, rotating machinery, gearboxes, bearings) the a-priori definition of
a decision strategy is somewhat easier. On a side note, a benefit of SHM
adoption is that it can also assist with general knowledge generation in this
process, for both standardized and non-standardized systems.

###  4.1 Optimizing a decision strategy

A decision strategy may be acquired through an optimization process.
Optimizing a decision strategy under uncertainty is performed by means of the
principle of maximum expected utility (Berger,, 1985). The goal is to identify
the optimal decision strategy $S^{*}$ that maximizes the expected utility:

$S^{*}=\operatorname*{arg\,max}_{S\in\mathscr{S}}\mathbb{E}_{\bm{X}}[U(S,\bm{X})],$
(1)

where $\mathbb{E}_{\bm{X}}$ is the mathematical expectation with respect to
the uncertain model parameters $\bm{X}$ and $U(S,\bm{X})$ is the utility over
the decision time horizon when strategy $S$ is implemented. The definition of
a decision time horizon is problem-dependent. In the context of structure and
infrastructure engineering systems, the time horizon of interest is often the
total life-cycle. In some cases, the total life-cycle can also be decided
based on the decision strategy, i.e., decommission/termination action or
replacement.

####  4.1.1 Bayesian decision analysis

In Bayesian decision theory, the uncertain state of the environment is
characterized by the vector $\bm{X}$, which includes uncertain quantities of
the physics-based model of the system, uncertain parameters of the
deterioration model, describing the temporal evolution of damage, and/or the
uncertain damage state. It is assumed that a prior probabilistic model of
$\bm{X}$ is available to the analyst. When the expectation in the strategy
optimization of Eq. 1 is performed with respect to the prior distribution of
$\bm{X}$, one refers to a prior decision analysis, whereby the SHM data are
not accounted for. As discussed above, when SHM data are merged with physics-
based models of the system and/or deterioration models, we refer to hybrid
models. In such a hybrid setting, SHM data can be used to update the knowledge
and reduce the uncertainty about the probabilistic model of $\bm{X}$. This
updating is performed via Bayesian inference (Gilks et al.,, 1995; Särkkä and
Svensson,, 2023). The definition of a likelihood function is required for
Bayesian inference; it relates the condition of the system to the data
obtained with the monitoring system (Bismut and Straub,, 2022). Examples of
likelihood functions are, e.g, a probability of detection curve for damage
detection (Long et al.,, 2022), or a probabilistic model of the discrepancy
between identified and model-predicted eigenfrequencies (Behmanesh et al.,,
2015). Once the probabilistic model of $\bm{X}$ is updated based on SHM data,
the expectation in the strategy optimization of Eq. 1 can be performed with
respect to the posterior distribution of $\bm{X}$. This renders a posterior
decision analysis, such as in the example of miter gate structures that
comprise inland waterway corridors (Vega and Todd,, 2022), and in the example
of railway infrastructure (Arcieri et al., 2023a, ). Recent work has also
extended modeling human risk perception in the decision process that biases
information provided by SHM (Chadha et al.,, 2023).

####  4.1.2 Value of Information (VoI) analysis

SHM data become available only after the actual installation and operation of
a SHM system. Nonetheless, one is often interested in investigating and
quantifying the potential economic benefit associated with SHM adoption in an
operational evaluation level. Such investigations can be performed within the
framework of a Value of Information (VoI) analysis (Pozzi and Kiureghian,,
2011; Straub,, 2014; Thöns,, 2018; Nielsen et al.,, 2021; Kamariotis et al.,,
2022; Giordano et al.,, 2022; Zhang et al.,, 2022), which entails a
preposterior Bayesian decision analysis (Raiffa and Schlaifer,, 1961). A VoI
analysis quantifies the expected improvement in decision-making due to the
reduced uncertainty offered by information sources. Specifically, the VoI
metric is quantified by the difference in expected total utility with and
without the SHM system. More specialized metrics, such as the Value of SHM
(VoSHM) (Andriotis et al.,, 2021; Kamariotis et al., 2023a, ) and the
normalized expected reward-to-risk ratio (Chadha et al.,, 2022) have been
introduced. A VoI/VoSHM analysis relies on simulation of future scenarios,
hence requiring a dedicated probabilistic model of the investigated SHM
system. This model is required for generating monitoring data that one expects
to extract from this SHM system for multiple sampled trajectories (sampled
from $\bm{X}$). Such a data generation process may be facilitated by use of a
probabilistic digital twin (Tsialiamanis et al., 2021b, ). The results of a
VoI/VoSHM analysis largely depend on prior knowledge related to the different
available models (see Section 3), operational conditions, environmental and
load variabilities, as well as the anticipated type of damages and mitigation
options that are of relevance for decision support. A VoI/VoSHM analysis can
be used as a tool to support decisions on whether or not to invest in
installation, or re-configuration of an SHM system (Kamariotis et al., 2023a,
), to optimize its design (Malings and Pozzi,, 2016; Cantero-Chinchilla et
al.,, 2020; Eichner et al.,, 2023), or to rank candidate options.

###  4.2 Methods for optimizing decision strategies

When used in long-term monitoring settings, SHM delivers a set of data in a
sequential manner at discrete points in time throughout the decision time
horizon. A temporal sequence of decisions on actions needs to be optimized.
This belongs to the class of stochastic Sequential Decision Problems (SDPs)
(Kochenderfer et al.,, 2022). The principle of maximum expected utility still
applies, however, optimizing a strategy involves taking into account future
actions and observations. Solution to stochastic SDPs can be cumbersome and
calls for large computational efforts.

Let us consider a simplistic decision setting, where one has to decide at each
time step $t_{k},\ k=1,\dots,n_{T}$ throughout a component’s life-cycle $T$
whether to repair (R) a component or do nothing (DN), in view of continuous
monitoring information (i.e., monitoring data are available at each $t_{k}$).
The set of possible actions at time $t_{k}$ is $a_{k}=\\{\text{R, DN}\\}$. The
sequence of actions throughout the life-cycle is
$\\{a_{1},\dots,a_{n_{T}}\\}$. Monitoring data obtained at each time step
affect the repair decision. In turn, the repair decision affects the state of
the component, and consequently also the decisions at future points in time.
This simple example aims to demonstrate the complex nature of stochastic SDPs.

Numerous frameworks and algorithms are available for solving stochastic SDPs
(Kochenderfer et al.,, 2022). In the context of maintenance planning for
structures and infrastructure systems, frameworks that have been employed for
the solution of SDPs include heuristic decision policies (Luque and Straub,,
2019; Bismut and Straub,, 2021), Markov decision processes (MDPs)/partially
observable Markov decision processes (POMDPs) (Papakonstantinou and
Shinozuka,, 2014; Memarzadeh and Pozzi,, 2016; Morato et al.,, 2022; Song et
al.,, 2022), and deep reinforcement learning (RL) (Andriotis and
Papakonstantinou,, 2019; Arcieri et al., 2023b, ). To effectively transfer
these frameworks in real-world applications, it is crucial to ensure that the
solutions they offer are interpretable, safe, and adhere to operational
constraints (Andriotis and Papakonstantinou,, 2021).

##  5 Increasing value creation for SHM

To date, for many structures and infrastructure systems, the O&M process is
mainly based on an ad-hoc usage of data. This makes it difficult to
demonstrate the effects of SHM on the life-cycle costs and the performance of
the systems, and to integrate it into standard operations and regulations. We
thus identify two main directions in which progress is needed to enhance the
SHM value generation. Firstly, since SHM provides its value by improving the
decision-making process, a more explicit consideration of the way in which SHM
informs decisions on O&M of systems is required. Secondly, improved
Verification and Validation (V&V) of SHM is essential for wider usage and
acceptance.

###  5.1 Integrating SHM into the decision-making process

Shifting to a data-driven and algorithmic-driven management entails convincing
owners and operators, as well as policy makers, to adopt advanced SHM
technologies and trust decision algorithms. This is a challenging process, as
for many systems it requires a shift from historically trusted rule-based
inspection and maintenance regimes to unproven performance-based philosophy
and regulations. This shift is particularly difficult for safety-critical
functionalities, where failures can lead to injuries and loss of life and
where prescriptive regulations often leave little room for reducing the
current level of inspections and maintenance activities.

It is crucial to better understand and formalize the maintenance strategies
that are currently in place, to facilitate a direct comparison of SHM-
supported decision processes against the existing (usually empirical)
approaches that the operators currently adopt. Getting the stakeholders
involved in understanding and formalizing the decision-making challenges can
help in better defining the utility function for decision support (e.g., by
taking into account the aversion of operators to unforeseen downtime).

Decision makers are often reluctant to adopt SHM because they assume that
their current traditional O&M strategy must be completely transformed by this
adoption, and that the rationale of the resulting strategy will not be fully
comprehensible to them. However, the integration of SHM data in the decision
strategy can occur gradually and in a controlled process. To investigate the
benefit of SHM, one can first assess the efficacy of the strategy currently
adopted by the decision makers, according to the excepted utility metric (as
illustrated above), and identify what changes are suggested on availability of
SHM data. By simulating evolution scenarios, one can assess the effectiveness
of “intermediate” strategies, which integrate the traditional assessment
regimes (e.g., visual inspection) and monitoring-driven suggestions. Then, the
decision maker can gradually implement some intermediate strategy and,
depending on the empirical effectiveness, as evidenced in the field, select
the appropriate integration level.

Provenance of predictions provided by prognostic algorithms must be presented
in an "understandable manner" to practitioners. Automated cost-effective
modeling processes and standardized options for instrumentation could help
lower the cost concern on the operators’ side. Operators also need an explicit
link between SHM-based diagnostic/prognostic indicators and types of actions
that are to be taken. The methods presented in Section 4.2 can also provide
this important mapping, from data to actions. Performance is also a key issue
as too many false alarms will undermine trust in the SHM system.

VoI/VoSHM analyses, as introduced in Section 4.1.2, determine the economic
benefit of deploying SHM on structures and infrastructure systems. Reliable
prognosis and models are needed for accurately quantifying the VoI, yet even
when models are inaccurate, VoI computation can be treated as an optimization
problem, informing SHM practices and options. VoI/VoSHM estimates are often
characterized by a large variability. An additional complication stems from
the fact that cost variables of a decision problem are themselves uncertain.
Stakeholders can again assist in defining preferences and costs for potential
consequences of different events and actions. It is essential that VoI
analyses are made transparent and convincing. There exist certain systems
where an obvious value exists in monitoring for preventing failure. There, SHM
can be supported even in absence of proof of VoI. Furthermore, putting a
precise number on the VoI is not as important as ensuring transition from
limited information and ad-hoc decisions to knowledge and data-supported
decision-making.

###  5.2 Verification and validation

Verification & Validation (V&V) is an essential, yet especially challenging
process to establish trust in the decision support capabilities of SHM
(Thacker et al.,, 2004). As of yet, V&V of SHM is not a well-understood
process, even in the more well-defined context of condition monitoring for
industrial assets. It requires the definition of high-level requirements and
then subsequently cascading these to lower tiers and eventually down to the
most granular levels, specifying the requisites for the performance of SHM
algorithms (Saxena et al.,, 2013). The section below describes the role and
interplay of SHM system requirements, system design, V&V, and finally
operations.

System Requirements: High-level system requirements comprise functional
prerequisites as well as non-functional performance criteria (e.g., safety,
availability) and cost requirements related to factors like damage,
unscheduled maintenance, and downtime, among others. It is essential to
establish methods for testing and verifying compliance with these
requirements, which often results in the creation of testability
prerequisites. These testability requirements may then extend to the
development of simulation models, testbeds, built-in-test modules, and
supplementary testing resources to be utilized during the verification phase.

Detailed System Design: This stage necessitates failure, risk and reliability
analyses to identify performance targets for health management, which should
influence the chosen SHM architecture. SHM design, when chosen to be deployed
from cradle-to-grave, must adhere to constraints cascading down from the
overall system design and operational requirements. These can include
specifications for model fidelity and computational complexity (when
considering a hybrid setting and use of a DT), sensor resolution, power
requirements, sampling rates, and more.

System Verification and Validation: A SHM system encompasses the
implementation of hardware and software components for managing relevant
sensors, signal conditioning/processing, and health management (diagnostic and
prognostic) algorithms. Ideally, during a verification phase, the SHM system
can be experimentally tested within supporting test platforms and tools
required for testing. In the PHM domain, and the monitoring of industrial
components and assets, it is common practice to simulate the relevant
environment at the system level to ensure the proper functioning of the
integrated system during various levels of operation, including injection of
faults. It is obvious that in the case of structural systems such tests at
actual scale are practically infeasible. This is where experimentation (in the
form of scaled testing, or hybrid-simulation (Gao et al.,, 2013)) can form a
crucial tool for V&V. Perhaps the highest value can be gained from
establishing and openly sharing data and experience gained from actual-scale
monitoring benchmarks (Peeters and De Roeck,, 2001; Maes and Lombaert,, 2021).
Given the uncertainty that is inherent in the systems on which SHM is applied
(incentivizing use of a SHM system), enhanced validation of SHM can be
achieved through a wide-spread application to a larger portfolio of structures
or components.

Operation and Maintenance: Assessment metrics should be in place to measure
SHM performance over time. The employed models will typically require
continual updating as both the structural and sensing system parameters change
over time. Parameters and thresholds related to SHM may need fine-tuning as
more information becomes available regarding the system’s response to
environmental and operational variabilities. Moreover, as improved
technologies become available, there may be a desire to apply updates to the
SHM system as well. Here, the VoI concept can serve as a potent tool for
quantifying the value of choices pertaining to system modifications or
upgrades.

##  6 Concluding remarks

This paper underscores the potential of Structural Health Monitoring (SHM)
systems to support decisions for operation and maintenance (O&M) of structures
and infrastructure systems, while also discussing multiple challenges that
arise in the process of materializing SHM in these contexts.

We first present a novel classification of SHM use cases along some principal
dimensions that relate to the nature of the decision-making task. Secondly, we
describe model classes that are required for enabling monitoring-informed
condition-based or predictive O&M planning. Specifically, we touch upon purely
data-driven models and hybrid models (including digital twins), and we comment
on the suitability of each model class in relation to the engineering system’s
characteristics (e.g., uniqueness, safety-criticality) and the corresponding
type of the available monitoring data (e.g., sufficient experimental data to
failure or not). Subsequently, we discuss the optimization of O&M decision
strategies under the availability of monitoring data and we describe relevant
computational frameworks and the Bayesian decision analysis scheme, which
forms the basis for Value of Information (VoI) and Value of SHM (VoSHM)
analyses.

Finally, we identify two key avenues to enhance the value of SHM. The first
avenue advocates the deepening of our understanding on the means and ways by
which SHM impacts the decision-making process. This is a crucial process that
requires the research community and the industry stakeholders to come together
to address several challenges. The latter usually show reluctance to adopt SHM
systems, but their input is defining as part of the SHM-informed decision
support process, e.g., via formalizing the maintenance strategies that are
currently in place and via determining the most relevant decision problems.
The second avenue focuses on rethinking the Verification & Validation (V&V)
process within the SHM context. Albeit a challenging and often poorly
comprehended procedure, V&V is of paramount importance towards a broader
acceptance and exploitation of SHM.

We believe that interdisciplinary, collaborative efforts, similar to the one
reflected in this position paper, are key to reaching a reliable synergy
between SHM and decision-making.

##### Funding Statement

This work received no specific grant from any funding agency, commercial or
not-for-profit sectors.

##### Competing Interests

The authors declare no competing interests exist.

##### Data Availability Statement

Data availability is not applicable to this article as no new data were
created or analyzed in this study.

##### Author Contributions

Conceptualization: A.K, E.C., D.S., N.D., K.G, A.J.H., G.L, C.P, K.G.P., M.P.,
M.T., K.W.; Writing - original draft: A.K.; Writing - review and editing:
E.C., D.S., N.D., K.G, A.J.H., G.L, C.P, K.G.P., M.P., M.T., K.W.; All authors
approved the final submitted draft.

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