Optimizing Financial Allocation for Maintenance and Rehabilitation of Munster’s Road Network Using the World Bank’s RONET Model

Lima, Mayara S. Siverio; Buttgereit, Alexander; Queiroz, César; Haritonovs, Viktors; Gschösser, Florian

doi:10.3390/infrastructures7030032

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…where the inequality is due to the assumption that relation (10) holds holds for horizon H. Also, using (11), we know that :…”

Section: Proof For Concavity Of Optimal Value Functionmentioning

confidence: 99%

“…Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs Hence, we can say that relation (10) holds for horizon H when s 0 ≤ d.…”

Section: Proof For Concavity Of Optimal Value Functionmentioning

confidence: 99%

“…This paper considers optimal planning for a class of structured budget-constrained POMDPs. This setting is motivated by infrastructure maintenance planning -a widely studied problem [9,10,11] that involves finding the optimal policy for maintenance and inspection, of an infrastructure component or a group of components within a certain budget [12,13]. For simplicity of planning, the stochastic dynamics of multi-component systems are modelled using a POMDP [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, we can say that for a horizon H + 1 and s 0 ≤ d, the value function V H+1 (s 0 , b) becomes constant in b for all b ≥ (H + 1)/2 . Now, to prove that relation (10) holds for s 0 ≤ d, we will consider two cases:…”

mentioning

confidence: 99%

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Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs

Vora¹,

Thangeda²,

Grussing³

et al. 2023

Preprint

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Partially Observable Markov Decision Processes (POMDPs) provide an efficient way to model realworld sequential decision making processes. Motivated by the problem of maintenance and inspection of a group of infrastructure components with independent dynamics, this paper presents an algorithm to find the optimal policy for a multi-component budget-constrained POMDP. We first introduce a budgeted-POMDP model (b-POMDP) which enables us to find the optimal policy for a POMDP while adhering to budget constraints. Next, we prove that the value function or maximal collected reward for a b-POMDP is a concave function of the budget for the finite horizon case. Our second contribution is an algorithm to calculate the optimal policy for a multi-component budget-constrained POMDP by finding the optimal budget split among the individual component POMDPs. The optimal budget split is posed as a welfare maximization problem and the solution is computed by exploiting the concave nature of the value function. We illustrate the effectiveness of the proposed algorithm by proposing a maintenance and inspection policy for a group of real-world infrastructure components with different deterioration dynamics, inspection and maintenance costs. We show that the proposed algorithm vastly outperforms the policy currently used in practice.

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“…where the inequality is due to the assumption that relation (10) holds holds for horizon H. Also, using (11), we know that :…”

Section: Proof For Concavity Of Optimal Value Functionmentioning

confidence: 99%

“…Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs Hence, we can say that relation (10) holds for horizon H when s 0 ≤ d.…”

Section: Proof For Concavity Of Optimal Value Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs

Vora¹,

Thangeda²,

Grussing³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, the proposed optimal design method can be practically utilized in quantity optimization processes tha require environmental performance evaluation. Since life cycle assessment (LCA) is be coming important in the construction sector related to buildings and bridges [51], the pro posed method could be used in the LCA by evaluating costs comprised of material pro duction and embodied CO2 emissions in future studies.…”

Section: Applicable Design Conditions Using Lm Indexmentioning

confidence: 99%

Non-Iterative Optimal Design Method Based on LM Index for Steel Double-Beam Floor Systems Reinforced with Concrete Panels

Choi

Kim

2022

Materials

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Steel double-beam floor systems reinforced with concrete panels can improve the structural and environmental performance of buildings by reducing moment demands and embodied CO2 emissions. However, for steel double-beam floor systems, a time-consuming iterative analysis is required to derive an optimal design proposal owing to the rotational constraints in the composite joints between the concrete panel and steel beams. In this study, a non-iterative optimal design method using the LM index is proposed to minimize the embodied CO2 emissions of steel double-beam floor systems. The LM index is a measure that can be used to select the optimal cross-section of the steel beams considering the decreased moment capacity according to the unbraced length. The structural feasibility of the proposed design method was verified by investigating whether safety-related constraints were satisfied by the LM index with respect to the design variables under various gravity loads. The applicability of the proposed optimal design method is verified by comparing the embodied CO2 emissions derived from the proposed and code-based design methods. Applicable design conditions were presented based on the LM index to aid engineers. The proposed design method can provide environmentally-optimized design proposals to ensure structural safety by directly selecting the LM index of steel beams.

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Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

Guerrieri,

Parla,

Khanmohamadi

et al. 2024

Infrastructures

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Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection.

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Optimizing Financial Allocation for Maintenance and Rehabilitation of Munster’s Road Network Using the World Bank’s RONET Model

Cited by 5 publications

References 15 publications

Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs

Welfare Maximization Algorithm for Solving Budget-Constrained Multi-Component POMDPs

Non-Iterative Optimal Design Method Based on LM Index for Steel Double-Beam Floor Systems Reinforced with Concrete Panels

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

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