**1. Introduction**

Structures are primarily designed to have the capacity to withstand certain loads they can be expected to bear. Over their design lives, structural members can be expected to deteriorate, with an attendant decrease in their capacities. This is generally as a result of exposure to environmental factors, aging of the constituent materials, as well as the residual effects of loading conditions. Such degradation of structures occurs in a number of ways including fatigue, corrosion, cracking, and scour. Thus, maintaining infrastructure to ensure its continued ability to fulfill design criteria is very important for the safety and security of a society. To this end, accurate knowledge of the in-service state of infrastructural assets cannot be trifled with. This brings to the fore, the pertinence of periodic inspection and maintenance for these assets, especially those designed to last for a long time, to ensure they continue to meet specified design requirements.

The process of inspecting or monitoring structures with a view to identifying and assessing damage to them is referred to as structural health monitoring (SHM). Principally, the inspection aspect is carried out by visual means. However, such visual inspections have a number of shortcomings including subjectivity of the inspecting personnel, the ability to only capture surface damage in visible locations, and the limitation of the inspection to damage without a congruent monitoring of loads applied to the structure. In addition, the sheer volume of infrastructural assets requiring such inspections and maintenance precludes the possibility of thorough inspections being carried out regularly on most, increasing the possibility of critical information getting missed during cursory examinations. For example, the Federal Highway Administration specifies biennial routine inspections for every bridge within its purview. These inspections are primarily designated to be carried out via visual inspection, with more advanced equipment and methods only deployed for more indepth inspection if deemed necessary. However, with about 600,000 bridges included in this inspection regime, rigorous visual inspection of each bridge becomes a very difficult task to achieve within the projected time frame, leading to increased risks of failure.

With the unreliability of such conventional lifetime assessment methods [1, 2], SHM has become a very important tool for assessing the lifetime of a structure. To curtail the stated shortcomings in the inspection and maintenance process, it became necessary to expand the original scope of SHM frameworks. In redefining the SHM process, Ref. [3] asserted that a monitoring system should include four operations namely acquisition, validation, analysis and management. Ideally, a single SHM system would collect information on both loads and system response to the loading. In addition to monitoring duties, an ideal SHM system would also incorporate some prognostic methods that will allow for the damage levels to be evaluated and the insitu health of the structures to be determined. This will allow for an analysis of the present and future performance of the infrastructural assets. Integrating a variety of disciplines including material science, non-destructive evaluation, fracture and fatigue mechanics, structural dynamics and structural design, SHM frameworks can be designed to collect information on deflections and strains, system behavior, thresholds of systems and members, input values for lifetime assessment, and maintenance planning [1]. To this end, SHM frameworks originally designed for capturing and assessing the initiation and propagation of damage, have been expanded to collect other information related to the performance of the structure [1, 4]. To prevent damage to structures, the monitoring or data acquisition stage of SHM is carried out principally using non-destructive evaluation (NDE) techniques. These techniques allow for the evaluation of the integrity of materials and structures without compromising their continued abilities to meet design criteria. Used widely for detecting and characterizing defects and damage in infrastructure, NDE techniques have been extensively researched and offer a useful means of collecting information for SHM to adequately characterize the in-situ health of infrastructure.

A holistic monitoring system must be able to provide sufficient information for users to make decisions on the continued serviceability of the structure, and on maintenance, repair and replacement regimens. With the plethora of tools developed for capturing damage and structural response, the determination of the presence of damage has become a relatively trivial task. However, the extrapolation of the effect of the damage on the serviceability of the structure remains a relatively complicated undertaking. Currently, most systems deployed for SHM of infrastructural assets are essentially either sensor arrays or other equipment used to identify the existence of

damage or distress in the structures under investigation, without requisite provision for quantifying the damage and defining the residual capacities of the structures. Thus, while providing good data on the loading of a system, and the damage incurred, these systems do not provide information on its continued serviceability, making manual inspections in the SHM process irreplaceable. To this end, SHM systems remain a precursor to physical inspections, giving inspectors an idea of the state of infrastructure and a basis for the determination of inspection and maintenance schedules. This brings to the fore the importance of accurately analyzing the data collected from the SHM systems.

Two approaches are used for the analysis and interpretation of the data collected, and primarily differ in the use or avoidance of a physics-based model for analyzing the behavior of the structure [5]. Model based approaches involve updates to models that capture damage and eroding capacity with respect to applied loads to reflect changes in the structural parameters observed from collected data [6–8]. In analyzing the strengths and weaknesses of the approaches, Ref. [5] determined that rather than being considered alternatives, both systems should be considered to be complementary, depending on the needs and requirements of the SHM system. Although well established, these methods are developed using idealizations of the structural behavior, without taking into considerations uncertainties in materials, geometry and loading conditions in analyzing the data collected. These uncertainties could lead to discrepancies between projected structural behavior and the actual behavior, possibly creating incorrect assumptions on the state of a structure. To this end, such deterministic methods remain at best approximations of the condition of a structure and must be used with caution. To overcome this shortcoming, a probabilistic process that accounts for these uncertainties, thus allowing for a more realistic estimation of the state of the structure, and its reliability can be applied. This inclusion of reliability methods in SHM processes, enhances the ability of monitoring systems and components in real time, and allows for the introduction of predefined alert levels to trigger specified actions once a value dips below a critical reliability index [5].

Beyond the use of reliability methods, Bayesian updating processes have become a popular means for updating the state of structures in SHM frameworks. These processes, utilize the data collected alongside prior knowledge on the performance of the structure to make inferences on its current state and future performance. The incorporation of Bayesian updating processes, moves SHM frameworks from being monitoring systems to becoming more holistic systems, inculcating both monitoring and analysis into making decisions on the condition and future performance of infrastructural assets.

Some steps are important for such SHM frameworks. These are obtaining data on the loading and response of structures, characterizing damage or distress from this information, analyzing the information, and making conclusions based on the characterizations. These steps are carried out to get a good grasp of the state of the structure, prior to full on-site inspections. However, with the cost of permanent sensors, it is quite unfeasible to instrument all infrastructural assets. The alternative, periodic inspections are also impractical due to the number and geographical spread of these assets. To counteract these challenges, this chapter proposes a trade-off between both. This alternative involves the use of existing and regularly updated data such as wind speeds, traffic information and ground motion from seismic events to extrapolate the condition of infrastructural assets exposed to these conditions, and update their conditions, allowing for the optimization of an inspection regimen. To this end, the objectives of this chapter include:


To meet these objectives, the rest of the chapter is designed to begin with a background and overview of the SHM process, and then focuses on a methodology designed to meet each objective. A case study example of traffic signal structures is included to demonstrate the use of the proposed methodology.
