Framework for Optimizing the Operation and Maintenance of Bridges ^†

Abstract

Ensuring the safety and reliability of bridges through the implementation of cost-effective asset management strategies is crucial for sustaining a resilient transportation infrastructure network. Thus, the utilization of structural health monitoring (SHM) was investigated to enhance the operation and maintenance of bridges, with a focus on best practices and strategic implementation. Specifically, this study aimed to explore methods that leverage SHM to enhance bridge-asset management practices. The existing literature on bridge asset management was used as a foundation for identifying the value of SHM in bridge operation and maintenance. While the general literature on SHM and asset management is extensive, very few studies effectively linked these two domains. By conducting a comprehensive review of relevant studies and successful integration cases, the results of this study offered the appropriate application of SHM in bridge asset management. The processes observed were then integrated into a strategic framework, serving as a practical guide for managers seeking to implement SHM effectively in bridge asset management, particularly during the operation and maintenance phase of the bridge lifecycle.

1. Introduction

Bridges are vital components of a nation’s infrastructure, and proper asset management is crucial to ensuring continued functionality, safety, and longevity. Optimal asset management is challenging, as the costs of these activities in a lifecycle must be minimized, while ensuring that the performance and reliability of the infrastructure related to the asset’s condition, serviceability, safety, and capacity are adequately addressed. Traditional bridge management often relies on periodic visual inspections and maintenance schedules. However, these methods may not adequately capture the dynamic condition of a bridge. To address this limitation, structural health monitoring (SHM) has emerged as a promising solution that provides real-time data about a bridge’s structural condition, enabling proactive and data-driven decision-making in asset management. SHM leverages sensor technology to continuously monitor the structural health of a bridge and provide real-time data for condition assessment. In this study, the literature was reviewed to see if the SHM technology can be employed successfully to manage infrastructure assets, specifically bridges and road bridges. The best practices of organizations that manage bridge assets during the operations and maintenance phase of the asset lifecycle are used to develop a framework of strategic bridge asset management processes to effectively integrate structural health monitoring into this critical phase of the asset lifecycle.

2. Literature Review

2.1. Overview of Asset Management

Asset management is used to ensure that an asset, such as a bridge, operates to enhance the asset’s life, optimizes maintenance and management costs, and meets present and long-term needs [1]. An asset management system, also referred to as an asset management framework, encompasses a set of interrelated elements designed to establish asset management policies, objectives, and processes, with the overarching goal of successfully achieving those objectives. [2]. Numerous asset management frameworks exist, each presenting distinctive methodologies to optimize the management of assets. Asset managers are encouraged to evaluate and adopt the most suitable approach based on the unique nature and context of their assets [3].

A fundamental commonality across all asset management systems is the concept of the asset lifecycle. This lifecycle encompasses all the stages an asset undergoes, from its inception to its eventual end-of-life [3]. Bridge asset management processes are integral components, seamlessly integrated into the four key phases that define the asset’s lifecycle:

• Planning;
• Acquisition and procurement;
• Operation and maintenance;
• Disposal and decommission.

The initial planning phase involves defining strategic objectives and goals for the asset. Asset management processes within this phase determine the asset’s intended purpose, long-term goals, and the allocation of resources. Asset management processes in the acquisition and procurement stage select the most appropriate assets, ensuring quality and compliance with the standards and optimizing the procurement and construction processes [4]. Once an asset is operational, the focus shifts to its ongoing operation and maintenance. Asset management processes in this phase are centered on the day-to-day management of the asset to ensure optimal performance. This includes preventive and corrective maintenance, as well as monitoring and adapting to changing needs and conditions [4]. The disposal and decommission phase marks the end of an asset’s lifecycle, during which assets are decommissioned, dismantled, or disposed of.

2.2. Core and Advanced Asset Management

The International Infrastructure Management Manual (IIMM) differentiates between core and advanced asset management practices to cater to organizations with varying levels of complexity and maturity in their asset management processes [5]. Core asset management ensures basic maintenance and compliance but may not fully leverage data-driven decision making or advanced technology. Advanced asset management involves a proactive, data-intensive, and strategic approach to asset management, often leveraging cutting-edge technologies and predictive analytics to optimize asset performance and enhance decision making. According to audits undertaken by the US Government Accountability Office (USGAO) and the New Zealand Office of the Auditor General (NZOAG), data gaps exist in existing asset management practices, hindering the development of advanced asset management practices [6,7].

2.3. Overview of SHM

SHM requires the strategic installation of sensors at critical locations in a structure to assess its overall structural health [8]. It involves measuring a structure’s operating and loading environment, along with its critical responses, to detect and evaluate signs of operational, serviceability, or safety issues, such as malfunctions or abnormalities [9]. The application of SHM extends to asset assessment, providing an immediate understanding of a structure’s health, monitoring the progression of structural deterioration, and expanding the safe operating range of structures [10]. According to the Imperial College London, damage detection methods vary depending on the SHM system’s intended use [11]. The damage assessment scale comprises four levels:

• Damage detection;
• Damage localization;
• Damage characterization (based on size and severity);
• Prognosis and estimation of the remaining useful life.

Each level of damage assessment requires a different number and location of transducers. In SHM, transducers are frequently integrated into sensors to capture data on the structural behavior of a given structure by converting physical quantities, including strain, pressure, temperature, or displacement, into electrical signals that are easily measured and analyzed. Early identification of problems through damage detection prevents severe damage, allowing for the strategic scheduling of maintenance tasks and avoiding unnecessary inspections. These objectives summarize the primary goals of structural health monitoring [8].

2.4. Value Assessment of SHM Systems

Before deploying an SHM system, it is crucial to assess its potential value. Nepomuceno et al. [12] developed an approach for assessing the value of an SHM system for a bridge before deployment. This assessment considers factors such as the bridge’s importance, condition, and expected service life. By evaluating the potential value of an SHM system, bridge owners and managers can make informed decisions regarding its implementation.

2.5. Applications of SHM in Bridge Asset Management

Various authors have effectively integrated SHM into bridge-asset management systems during the operation and maintenance phase of their assets’ lifecycle. Figueiredo et al. [13] used SHM to make informed decisions about bridge maintenance, repair, and rehabilitation, validating their approach with real-world bridge data. Ni and Wong [14] designed an integrated SHM and maintenance management system for sea-crossing viaduct bridges, optimizing maintenance based on criticality and vulnerability. Seo et al. [15] conducted a summary review of SHM applications for highway bridges, highlighting the benefits of SHM in detecting and monitoring structural deterioration, assessing the remaining service life, and guiding maintenance decisions. Phares [16] developed a bridge asset management system that leveraged SHM to extend service life and reduce costs, rendering it economically justifiable. Dubbs [17] demonstrated the value of SHM integration by illustrating its ability to provide actionable information in areas such as maintenance work orders, operations, public safety, structural safety, and security, ultimately paving the way for an advanced asset management system.

2.6. Condition-Based, Predictive, and Preventive Maintenance

Flanigan et al. [18] proposed a reliability-based framework for linking long-term monitoring data to condition ratings. This framework adopted reliability analysis techniques to assess the condition of bridges based on the collected monitoring data, thus enabling condition-based maintenance (CBM). By quantitatively linking monitoring data to condition ratings, bridge owners and managers can prioritize maintenance and allocate resources effectively only when asset conditions dictate its necessity. Predictive maintenance modeling contributes to the future network-wide management of bridges. Stevens et al. [19] reviewed various approaches in bridge predictive maintenance modeling and outlined the challenges in their adaptation. In these models, data from SHM systems were used to predict the future performance and deterioration of bridges, enabling proactive maintenance strategies. While SHM is often associated with predictive maintenance, it also supports preventive maintenance by creating condition thresholds or triggers that determine when inspection maintenance activities must be carried out. These thresholds are based on observed data patterns and allow maintenance teams to address minor issues before they escalate.

2.7. Risk and Criticality in Data Collection

Bush et al. [20] developed a data collection and monitoring strategy for the asset management of road bridges. In this approach, bridges within a network were classified into one of three data collection categories: core, intermediate, or advanced. The classification considered the risk profile and the criticality of each bridge in the network, and a simple scoring scheme was proposed. Depending on a bridge’s criticality level and risk, its data collection technique was shifted from core, to intermediate, to advanced. Correspondingly, the bridge is moved towards higher levels of data collection that involve collecting more detailed and reliable data. The higher data collection categories rely on proactive and planned integration of SHM systems for asset management. By providing a flexible approach to data collection, the bridge asset manager can tailor the strategy to align with local network requirements, risk tolerance, and budget, thereby ensuring the cost efficiency of strategy implementation.

3. Proposed Framework

A bridge asset management framework that effectively integrates structural health monitoring techniques into the operation and maintenance phase of the asset lifecycle is illustrated in Figure 1. This framework encompasses the following key activities and processes: value assessment, criticality assessment, risk assessment, asset management strategy, performance monitoring and condition assessment, and maintenance optimization.

3.1. Value Assessment

Objectively determine the state of the bridge after a visual inspection has deemed it structurally inadequate. Assess the value of implementing an SHM system for the bridge by evaluating factors such as the bridge’s significance within the network, current condition, and expected service life. This initial assessment will help determine whether SHM is warranted for the specific bridge.

3.2. Criticality Assessment

Perform a criticality assessment to determine the importance of each bridge within the network. Criticality is often based on factors such as the bridge’s role in the transportation network and its potential impact on public safety.

3.3. Risk Assessment

Assess the potential risks associated with the observed structural anomalies, taking into account factors such as traffic load, weather conditions, and material properties. Assign the level of risk to each observed anomaly based on its potential impact on safety and structural integrity. This assessment helps prioritize bridges for more detailed monitoring and maintenance efforts.

3.4. Asset Management Strategy

Align the asset management strategy with the specific needs of the bridge to ensure that the chosen strategy is tailored to the bridge’s unique characteristics. Evaluate the need for advanced asset management based on the bridge’s condition, demand, and importance within the network. Apply a simple asset management approach for low-risk and low-criticality bridges and advanced asset management for high-risk and high-criticality bridges.

3.5. Data Collection

Classify bridges within a network into one of three data collection categories (core, intermediate, or advanced) by considering the risk profile and criticality of each bridge within the classification. Core bridges require less frequent visual inspections and limited, usually reactive SHM. Intermediate and advanced bridges will require more frequent visual inspections and proactive, planned integration of SHM systems accordingly.

3.6. Performance Monitoring and Condition Assessment

With the data collection and monitoring strategy in place, establish a performance monitoring and condition assessment program that links SHM data to condition ratings. This is essential in ensuring that the collected SHM-based monitoring data are leveraged to evaluate the condition of bridges and enable condition-based maintenance.

3.7. Maintenance Optimization

Based on the condition assessment, maintenance and repair activities are optimized to balance the cost of repairs and the preservation of the bridge’s structural integrity. This includes scheduling repairs when necessary, allocating resources efficiently, and reducing network interruption. For bridges in a relatively healthy condition, a predictive maintenance approach is suitable, using real-time monitoring and data analysis to efficiently anticipate and address emerging issues and avoid unnecessary maintenance work. For bridges with identified structural issues, vulnerabilities, or a higher risk profile, preventive maintenance is appropriate, employing scheduled, routine maintenance to prevent potential problems. This data-driven approach ensures that maintenance efforts are focused on critical areas of the bridge, thereby extending its service life.

4. Conclusions

This study provides a foundation for adopting SHM in bridge asset management, emphasizing the potential benefits of this technology in managing bridge assets. The presented framework offers a data-driven and proactive approach for enhancing bridge asset management processes during the operation and maintenance phase of the bridge lifecycle. This framework ensures that the assessment of value, criticality, and risks guides the data collection strategy, enabling targeted SHM implementation, optimal resource allocation, and effective condition-based maintenance strategies, tailored to each bridge within a network. Similarly, the asset management strategy guides the data collection strategy, ensuring the necessary data are available to implement the required asset management approach. By implementing this framework, stakeholders involved in bridge asset management can be better equipped to use SHM to make informed decisions regarding operation, maintenance, repair, and rehabilitation.

Shaping the future of bridge asset management via SHM remains imperative. Future research needs to validate the framework’s effectiveness and adaptability for the unique characteristics of different bridge structures. Additionally, the development of standardized guidelines and best practices for SHM integration must be an ongoing priority, enabling bridge authorities and asset managers to make well-informed decisions. In summary, enhancing bridge asset management through SHM is a dynamic and ever-evolving field, with the potential to transform how we monitor, maintain, and extend the life of critical infrastructure. The framework presented in this study represents a significant step toward more efficient, data-driven, and proactive bridge asset management.