5 research outputs found
Incorporating pavement deterioration uncertainty into pavement management optimization
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Pavement Engineering on 2022, available online: https://www.tandfonline.com/doi/full/10.1080/10298436.2020.1837827[EN] Pavement management systems can be used to efficiently allocate limited maintenance budgets to better align with pavement deterioration. However, pavement deterioration is subject to uncertain factors that complicate the prediction of future pavement conditions accurately, entailing differences in the optimum maintenance strategy. This paper addresses this challenge by introducing a method to aid local engineers in optimising the scheduling of maintenance activities under uncertain pavement deterioration conditions. Markov chains are used to simulate the variability of life-cycle performance. Moreover, a multi-objective optimisation of an urban network is carried out to find the maintenance programme that minimises the mean life-cycle cost, maximises the mean user benefit, and minimises the standard deviation of life-cycle cost. This third objective enables the optimisation routine to minimise the possibility of unintentionally increasing the life-cycle cost due to system variability. This approach results in a reduction of the life-cycle cost variability by up to 62%, provides pavement strategies that benefit road users as a result of better pavement conditions, and reduces the risk of resorting to costly future maintenance activities.This work was supported by the Spanish Ministry of Science and Innovation with the European Regional Development Fund (grants BIA2017-85098-R and RTC-2017-6148-7).GarcĂa-Segura, T.; Montalbán-Domingo, L.; Llopis-CastellĂł, D.; Lepech, MD.; Sanz-Benlloch, MA.; Pellicer, E. (2022). Incorporating pavement deterioration uncertainty into pavement management optimization. International Journal of Pavement Engineering. 23(6):2062-2073. https://doi.org/10.1080/10298436.2020.18378272062207323
Recommended from our members
Integrated planning and budget allocation for highway maintenance, rehabilitation, and capital construction projects
Highway infrastructure is one of the critical components of the infrastructure network needed for the socio-economic development of a country. However, increased urbanization, limited funds, the need to consider sustainability continue to challenge the planning process for developing and maintaining highway infrastructure. Accordingly, decision-makers are tasked with making optimal decisions while achieving the strategic goals set by federal, state, district, and/or local highway agencies. Pivotal to making such resource allocation decisions, is the availability and accuracy of asset-related data and planning constraints which can guide data-driven decisions to be made by State Highway agencies (SHAs). Currently, several decision-makers still depend significantly on subjective engineering judgment to make decisions on funds allocation. Hence, there is a need for more formal and logical approaches to resource allocation as well as evaluation metrics for conducting alternatives analysis.
This notwithstanding, the development of multiple incompatible legacy systems and the presence of several funding categories with stringent project eligibility requirements underpins a “siloed” approach to planning for highway infrastructure. There are often multiple functional groups working on the same asset network but with heterogeneous information systems and distinct decision-making practices. This “siloed” approach can create inefficiencies in projects selection and lead to inter-project conflicts in the highway projects proposed by these different functional groups. When left unaddressed, these spatial-temporal conflicts among projects can result in the misuse of limited taxpayer dollars and ultimately, a lower performance of the network.
To address these issues with budget allocation and integrated highway planning, this study contributes to the body of knowledge in three primary ways. First, the study provides a synthesized analysis of budget allocation methods and provides a comprehensive approach to evaluating the performance of different methods employed for M&R decision-making. Secondly, this study formulates and accounts for the impact of multiple funding categories and project eligibility restrictions in budget allocation models. The inclusion of this pragmatic characteristic of M&R decision-making demonstrates the inefficiencies that can result from having increasing restrictions on multiple funding categories. Thirdly, a shared ontology is developed to enable a dynamic link between planning information and projects information. The resulting formalized representation (ontology) was validated by using multiple approaches including automated consistency checking, task-based evaluation, and data-driven evaluation. An implementation tool was also developed and applied to an actual case study problem. The tool was validated by using a Charrette test and feedback from subject-matter experts.Civil, Architectural, and Environmental Engineerin
Optimal sensor placement for sewer capacity risk management
2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research
Recommended from our members
Network-level maintenance decisions for flexible pavement using a soft computing-based framework
An effective pavement management system (PMS) is one that is guided by a software program that ensures that all pavement sections are maintained at adequately high serviceability levels and structural conditions with a low budget and resource usage, without causing any significant negative effect on environment, safe traffic operations and social activities. PMS comprises of section classification; performance prediction; and optimisation for decision-making. For section classification, this research presents a fuzzy inference system (FIS), with appropriate membership functions for section classifications and for calculating the pavement condition index (PCI). The severity and extent of seven distress types (alligator cracking, block cracking, longitudinal and transverse cracking, patching, potholes, bleeding and ravelling) were used as fuzzy inputs. The result showed a good correlation for fuzzy model. A sensitivity analysis showed a pavement crack has the greatest influence on section classification compared to the other distress types. A novel network level deterministic deterioration model was developed for flexible pavement on arterial and collector roads in four climatic zones considering the impact of maintenance, age, area and length of cracks, and traffic loading. The prediction models showed good accuracy with high determination coefficient (R2). The cross-validation study showed that the models for arterial roads yield better accuracy than the models for collector roads. A sensitivity analysis showed that the area and length of cracks have the most significant impact on the model performance. A novel discrete barebones multi-objective particle swarm algorithm was applied for a discrete multi-objective problem. Conventional particle swarm optimisation (PSO) techniques require a manual selection of various control parameters for the velocity term. In contrast, the bare-bones PSO has the advantage of being velocity-free, hence, does not involve any parameter selection. The discrete barebones multi-objective PSO algorithm was applied to find optimal rehabilitation scheduling considering the two objectives of the minimisation of the total pavement rehabilitation cost and the minimisation of the sum of all residual PCI values. The results showed that the optimal maintenance plan found by the novel algorithm is the better than found by conventional algorithm. Although the results of performance metrics showed that the both algorithms perform on a par, the novel algorithm is clearly advantageous as it does not need parameter selection