Minimizing the global warming impact of pavement infrastructure through reinforcement learning

Renard, S.; Corbett, Benjamin; Swei, Omar

doi:10.1016/j.resconrec.2020.105240

Cited by 30 publications

(6 citation statements)

References 39 publications

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“…China is on track to achieve "peak carbon" and "carbon neutrality" by 2030 and 2060, respectively, which has prompted environmental managers and policymakers to focus on the carbon emissions of products and services in general. A significant portion of greenhouse gas emissions come from our extensive paved road infrastructure system [53]. The calculation method for carbon emissions in this paper is shown in Equation ( 28): carbon emissions = carbon emission factor × level of activity (28) Among them, the carbon emission factors include greenhouse gases generated by energy consumption, material production processes, and the work of machinery or equipment.…”

Section: Carbon Emissionsmentioning

confidence: 99%

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

Xu,

Wang,

Kang

et al. 2024

Sustainability

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By choosing the right pavement maintenance plan, we can reduce resource utilization, reduce environmental pollution, and extend road life, which is important for improving social sustainability. At present, the selection of road maintenance programs mostly adopts multiple attribute decision-making (MADA), in particular, the analytic hierarchy process (AHP) is often used. However, this method needs to use expert scoring data, which leads to strong subjectivity and poor reliability. Therefore, it reduces the science of road maintenance scheme selection. In order to reduce the subjectivity of the score and obtain a more suitable road maintenance scheme, this paper applies a multi-criteria decision-making method that characterizes attribute information by triangular fuzzy numbers (TFN) in the discrete decision space. Firstly, we invite experts to score the importance of the selection of pavement preventive maintenance technical solutions with respect to the indicators affecting the selection of solutions. Secondly, the two indicators of similarity and reliability are used to quantitatively evaluate the indicators and programs, respectively. Finally, we compare the weighted programs according to the overall possibility degree of each program. In actual cases, the overall possibility degree of each scheme obtained by this method is 1.0002–0.0477, and the optimal solution is fog sealing technology. The decision-making model applied in this paper can be considered in multiple dimensions, which can scientifically reduce the subjectivity of expert scoring. The best maintenance plan can also be quickly obtained through the simple calculation method in this paper.

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Section: Carbon Emissionsmentioning

confidence: 99%

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

Xu,

Wang,

Kang

et al. 2024

Sustainability

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“…Combining treatments and developing reliable schedules can significantly contribute to these outcomes [26][27][28]. Additionally, the timing of maintenance treatments is crucial, and developing maintenance schedules based on long-term deterioration research has been the focus of extensive studies [29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning, a popular technique, has been widely used for damage recognition, crack detection, and prediction [37][38][39][40]. Reinforcement learning has emerged as a recent method to maximize cost-effectiveness in determining ideal treatment schedules [32,41]. Reinforcement learning (RL) can be applied to overcome decision-making challenges in long-term maintenance.…”

Section: Related Workmentioning

confidence: 99%

“…Pavement authorities face difficulties stemming from the intricate nature of pavement deterioration, the presence of alternative treatments, and the utilization of diverse pavement performance indicators. Machine learning techniques, particularly reinforcement learning, offer a sequential decision-making approach for large-scale simulations and optimization of long-term maintenance planning [32,[42][43][44][45][46][47][48][49][50] as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

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A Deep Learning Technique to Improve Road Maintenance Systems Based on Climate Change

et al. 2023

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Road maintenance systems (RMS) are crucial for maintaining safe and efficient road networks. The impact of climate change on road maintenance systems is a concern as it makes them more susceptible to weather events and subsequent damages. To tackle this issue, we propose an RMSDC (Road Maintenance Systems Using Deep Learning and Climate Adaptation) technique to improve road maintenance systems based on Deep learning and Climate Adaptation. RMSDC aims to use the multivariate classification technique and divides the dataset into training and test datasets. The RMSDC combines Convolutional Long Short-Term Memory (ConvLSTM) techniques with road weather information and sensor data. However, in emerging nations, the effects of climate change are already apparent, which makes road networks particularly susceptible to extreme weather, floods, and landslides. Therefore, climate adaptation of road networks is essential, especially in developing nations with limited financial resources. To address this issue, we propose an intelligent and effective RMSDC that utilizes deep learning algorithms based on climate change predictions. The ConvLSTM block effectively captures the relationship between input features over time to calculate the root-mean deviation (RMSD). We evaluate RMSDC performance against frameworks for downscaling climate variables using two metrics: root-mean-square error (RMSE) and mean absolute difference. Through real evaluations, RMSDC consistently outperforms approaches with a reduced RMSE of 0.26. These quantitative results highlight how effective RMSDC is in addressing maintenance systems on road networks leading to proactive road maintenance strategies that enhance traffic safety, reduce costs, and improve environmental sustainability.

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“…Renard et al developed a reinforcement learning (RL) decision support tool that minimizes the global warming impacts of a pavement system over its life cycle. Renard et al presented an approach to LCA modelling that implements a reinforcement learning algorithm called Q-learning, which helps decision-makers account for several sources of uncertainty in pavement infrastructure (Renard et al 2021a).…”

Section: Other Types Of MLmentioning

confidence: 99%

Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

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Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

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Minimizing the global warming impact of pavement infrastructure through reinforcement learning

Cited by 30 publications

References 39 publications

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

A Deep Learning Technique to Improve Road Maintenance Systems Based on Climate Change

Advances in application of machine learning to life cycle assessment: a literature review

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