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Reinforcement learning (RL) has recently been adopted by infrastructure asset management (IAM) researchers for adding flexibility regarding uncertainties in preventive actions decision-making. However, this relatively recent line of research has not incorporated other sources of uncertainties, such as hazards apart from deterioration patterns, nor has such research considered managerial aspects of IAM, such as stakeholders' utilities. This paper aims to provide a holistic framework that draws upon recent developments in IAM systems and microworlds, employs RL model training, and considers deterioration, hazards, and cost fluctuations as the main sources of uncertainties while also adopting managerial aspects into decision-making. Consistent with the existing practice of IAM, this framework brings flexibility in the face of uncertainties to the IAM decision-making process. Multi-agent RL models based on deep Q networks and actor-critic models are constructed and trained for taking intervention actions regarding elements of a real bridge in Indiana through its life cycle. Both models could lead to higher expected utilities and lower costs compared to the optimal maintenance, rehabilitation, and reconstruction (MRR) plans obtained by Monte Carlo simulation and heuristic optimization algorithms. The proposed framework can assist decision-making bodies and managers in the IAM domain with making updateable optimal and more realistic decisions based on the updated state of various complex uncertainties in a negligible amount of time. Long-term strategic intervention actions are vital for maintaining the safety of degraded infrastructure assets. Traditionally, these strategic plans are derived through using optimization methods for maximizing stakeholders' utilities, improving the safety of the network, and minimizing costs and risks associated with catastrophic events. The main drawback, and perhaps the biggest reported challenge, of this approach is the lack of flexibility in the face of the updated state of uncertain phenomena, such as the condition of assets and volatile costs. Simply put, the rare outcomes of unprecedented events can render optimal strategies useless. This study promotes the application of artificial intelligence-based agents for taking updateable decisions given the newly-observed state of all uncertain factors. Based on the results of the case study, the proposed framework was found able to reduce costs by up to 7% during the management horizon. This reduction in costs can be translated into saving millions of dollars of taxpayers' money on a community scale. The theoretically well-founded and practically applicable proposed framework could measurably enhance the management of various infrastructure assets. Given that it can be tailored to other decision-making processes under uncertainty, the proposed framework can be similarly applied to other complex managerial problems. [ABSTRACT FROM AUTHOR]

Probabilistic and nonlinear models have been used to accurately model various phenomena in asset management systems (AMS). With a commonly adopted framework using Monte Carlo simulation and heuristic algorithms, AMS proposed in the literature aim to maintain the functionality of assets in their life-cycle by optimally allocating limited resources to different intervention actions. However, due to their high computational costs, upscaling complex project-level AMS to a multitude of assets currently is far from practical. To address this gap between the literature and the practice of project-level AMS, this paper presents a new machine learning–based methodology to estimate (near-)optimal intervention timings which usually are derived by optimization algorithms. To illustrate, an ensemble of random forests models was trained on optimal maintenance timings of more than 1.6 million semisynthesized bridges. The trained model yielded optimized maintenance, rehabilitation, and reconstruction (MRR) plans with greater than 95% accuracy on the test set and greater than 89% accuracy on more than 4,600 highway bridges in Indiana, and did so 6 orders of magnitude faster than the conventional framework of complex MRR optimization. Practitioners can adopt the proposed methodology to enhance their decision-making systems, obtain optimal maintenance plans without sacrificing complex and accurate models, and take another step toward sustainability objectives. [ABSTRACT FROM AUTHOR]

Given the importance of maintaining infrastructure assets, asset management at both project- and network-levels has been the focus of hundreds of studies. Although these studies and previously developed asset management systems have pushed the boundaries of this field, these advances have been made separately without the ability to build upon one another. This paper puts forward the first open-source, extensible, freely accessible, and modular platform developed in Python, GIAMS, paving the way for researchers and practitioners to easily collaborate and contribute to future related research. Drawing upon related literature, this paper describes modules of GIAMS and illustrates their use with bridge components and models. One project-level lifecycle optimization and one network-level project-selection based on the Indiana, US, bridge network with more than 4600 bridges are provided to demonstrate the applicability of GIAMS. The modular and open-source nature of GIAMS makes it readily capable of further extension in a variety of asset management topics. • The first open-source and extensible platform is developed for asset management. • More than 4600 bridges are used to show the applicability of the developed platform. • The extensibility of GIAMS is demonstrated through three different examples. • GIAMS can accelerate and facilitate research in infrastructure asset management. • Practitioners can use an up-to-date and freely accessible asset management platform. [ABSTRACT FROM AUTHOR]