Pavement Maintenance Decisions Research Articles

MILP-Based Approach for High-Altitude Region Pavement Maintenance Decision Optimization

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas.

Multi-Objective Optimization for Sustainable Pavement Maintenance Decision Making by Integrating Pavement Image Segmentation and TOPSIS Methods

To provide a low-carbon economy maintenance strategy is the most challenging problem faced by pavement management authorities under the restricted budget and significant environmental repercussions. The development of a multi-objective optimization model for pavement maintenance decision making is essential to formulate pavements. Nevertheless, the existing automatic detection can only recognize and classify pavement distress. However, few studies are able to accurately determine the precise dimensions of specific distresses such as cracks and potholes, especially combined with the actual size of the image. This limitation hinders the ability to provide specific maintenance recommendations and make optimal maintenance decisions. Therefore, this paper develops a comprehensive and effective multi-objective decision-making framework for pavement maintenance. This framework consists of four distinct components: (1) recognizing the dimensions of pavement distresses based on the pavement image segmentation technique; (2) compiling a list of viable pavement maintenance strategies; (3) assessing the costs and carbon emissions of these strategies; and (4) optimizing decisions on pavement maintenance. We used the U-Net algorithm to accurately recognize the dimensions of pavement distresses, while an improved entropy-weighted TOPSIS model was proposed to determine the optimal pavement maintenance strategy with the lowest cost and carbon emissions. The results indicated that the pavement distress dimension recognition model achieved a high accuracy of 96.88%, and the TOPSIS model identified the optimal maintenance strategy with a score of 99.16. This maintenance strategy achieved a substantial reduction of 30.80% in carbon emissions and a cost reduction of 20.81% compared to the highest values among all maintenance strategies. This study not only provides a scientifically objective method for making pavement maintenance decisions but also offers specific, quantifiable maintenance programs, marking a stride towards more environmentally friendly and cost-effective road maintenance. It also contributes to the sustainability of pavement maintenance.

Enhancing pavement maintenance: A deep learning model for accurate prediction and early detection of pavement structural damage

Pavement distress, which is exacerbated by structural damage, results in massive carbon-intensity for routine maintenance and transportation safety issues. One promising approach to resolving this issue is to predict structural damage before it occurs. However, structural damage prediction is a complex problem with many influential factors that no single index can precisely solve for in-service pavement. In this study, a new method is adopted that combines all implicit factors into a deep learning neural network (DNN) algorithm to achieve high accuracy for the prediction of pavement structural damage. The training and optimization procedures of the DNN model are introduced explicitly for model repeatability and development. The results show that the state-of-the-art (SOTA) model performed well, with an accuracy rate of 88.46%. The contribution of this research facilitates the development of pavement maintenance decision systems to provide a prediction method for on-site pavement structures. By employing this approach, structural damage can be identified early to prevent further expansion, which will reduce costs and save lives and natural resources.

Optimization for Asphalt Pavement Maintenance Plans at Network Level: Integrating Maintenance Funds, Pavement Performance, Road Users, and Environment

As an infrastructure project, the cost and benefits of road maintenance should be measured through social costs. In order to fully consider user interests and make road maintenance decisions more reasonable, two types of user costs that may affect road maintenance benefits have been quantified through analysis of existing literature. At the same time, environmental issues have gradually become the focus of various industries, and in optimizing road maintenance decisions, the impact of environmental issues on decision making should also be considered. This article first analyzes and quantifies the user costs that affect the effectiveness of road maintenance. Secondly, based on the concept of sustainable development of roads, the optimization of road maintenance decisions is divided into two steps: the first step is to determine the minimum maintenance budget funds, and the second step is to determine the optimal plan. The specific optimization method is to use the 0-1 mathematical programming method to establish a network-level pavement maintenance decision optimization model based on a quantitative model. The most reasonable maintenance optimization plan is determined from four aspects: maintenance funds, maintenance performance improvement value, user benefit improvement, and reducing environmental impact. Finally, a provincial road network for a case study is selected. The applicability of the new model is verified through a case study. This study can help decision makers deal with asphalt pavement maintenance arrangements at the network level with four decision objectives: maintenance funds, pavement performance, road users, and environmental impacts.

Safety Integrated Network Level Pavement Maintenance Decision Support Framework as a Practical Solution in Developing Countries: The Case of Addis Ababa, Ethiopia

Every year, road traffic crashes lead to the loss of 1.35 million lives globally, of which ninety percent happens in developing countries. Moreover, the rapid deterioration of road infrastructure in these countries causes substantial economic losses and exacerbates road safety issues. This paper proposes a practical and safety-incorporated approach to implementing a strategic pavement management system to address pertinent problems. A two-tiered stochastic process of pavement deterioration and repair is modeled using a Markov-based model. The proposed model is suitable for road agencies with limited road condition data. Road safety conditions considering all road user groups are measured and analyzed using the international road assessment program. The paper outlines a process for establishing appropriate pavement and safety performance goals, developing a deterioration prediction model, and analyzing the relative life cycle cost and risk of maintenance strategies to achieve dual performance goals—pavements and safety. A case study of a road network in Addis Ababa, Ethiopia, illustrates the effectiveness of the proposed approach, showing a significant reduction in fatal and serious injuries by up to 60% annually. In this regard, the proposed approach is useful for road agencies to make informed and safety-conscious decisions to manage their assets proactively with relatively less pavement condition data to ensure safe roads.

Modeling of Pavement Maintenance Decisions Using Artificial Intelligence Based on Maintenance Unit.

Recently, all efforts have been directed toward keeping the network functional at a high level by determining the appropriate maintenance or rehabilitation (M & R) treatment

Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to perform necessary maintenance activities to achieve and maintain high levels of service. Pavement maintenance can typically be very expensive and decisions are needed concerning planning and prioritizing interventions. Data are key towards enabling adequate maintenance planning but in many instances, there is limited available information especially in small or under-resourced urban road authorities. This study develops a roadmap to help these authorities by using flexible data analysis and deep learning computational systems to highlight important factors within road networks, which are used to construct models that can help predict future intervention timelines. A case study in Palermo, Italy was successfully developed to demonstrate how the techniques could be applied to perform appropriate feature selection and prediction models based on limited data sources. The workflow provides a pathway towards more effective pavement maintenance management practices using techniques that can be readily adapted based on different environments. This takes another step towards automating these practices within the pavement management system.

Optimization Model of Network‐Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs

The maintenance management decisions of network‐level asphalt pavements have long been a challenge to highway agencies, and a great amount of factors have been involved. In this study, a network‐level optimization method was established by integrating the maintenance benefits into the zero‐one programming optimization model. An optimized performance evaluation method of asphalt pavement was proposed which contains 11 different kinds of combinations. The benefit model quantifies the cost savings of user travel time and vehicle fuel consumption to the pavement condition index (PCI) and ride quality index (RQI), respectively. Based on the simplified evaluation method as well as the quantified maintenance benefit model, an optimization model was established by employing the zero‐one programming. This optimization model aimed to maximize the improvements/price ratio of pavement maintenance for the whole pavement network. The calculation results present the optimal strategies of maintenance for every road section in the network. The applicability of the newly proposed model was validated by a case study. The methodology developed in this study helps to offer guidelines to highway agencies in managing and making decisions about network‐level pavement maintenance.

A machine learning methodology to predict alerts and maintenance interventions in roads

This contribution is about predicting maintenance alerts in roads and selecting the most appropriate type of interventions recommended for preventing the occurrence of future failures. The objective is aligned with that covered by pavement maintenance decision support systems (PMDSS), though the methodology presented can be applied to other non-pavement road linear assets. The purpose is to summarise the main findings in the development of an approach based on testing the four most extended machine learning techniques (ML), namely Decision Trees (DT), K-Nearest Neighbourhood (KNN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN), using data from the historical inventory of inspections and maintenance interventions of a case study to illustrate the potential that such approach can offer to road maintenance managers. The correlation process embodies supervised and unsupervised training of models. The maintenance predictions are presented and compared over various segments corresponding to the real maintenance interventions conducted on an existing road network of a geographical zone.

Road Crack Detection Using Deep Neural Network with Receptive Field Block

Cracks are common pavement diseases that affect pavement performance. To maintain the road in good condition, localizing and fixing the cracks is a vital responsibility for transportation maintenance department. However, traditional manual detection methods are considerably tedious and require domain expertise. Therefore, the research on automatic detection and identification of pavement crack is of great significance for ensuring traffic safety and pavement maintenance decisions. In this paper, we propose an automatic pavement crack detection network based on the Single Shot MultiBox Detector(SSD) deep learning framework, and introduce the receptive field module to enhance the feature extraction capability of the network, which ensures real-time crack detection and also improves the performance of accuracy in pavement crack detection.

Segment-based pavement crack quantification

Abstract Accurate quantitative information of crack length and width is important for assessing the severity level of cracks and making accurate pavement maintenance decisions. Nevertheless, due to the noise and fluctuation at the crack edge of pavement image, itis difficult to precisely determine the edge correspondence of crack that is necessary for measuring the crack width at a specific pixel. Different automated or manually performed measurements are likely to yield different results at the same pixel. Instead of measuring the crack width on a pixel-by-pixel basis, this paper presents an accurate and robust segment-based method for measuring crack width. Firstly, based on the distinctive curved structure of pavement crack, a structured edge detector is trained to obtain the confidence map of crack edges. Secondly, with the crack edge map, the morphological operation is used to extract the crack skeleton which characterizes the propagation of the crack. Adaptive segmentation of the crack skeleton is performed to partition a crack curve into crack segments. Each crack segment has the same width because its edges become almost parallel after the segmentation. Finally, combining the structured edge confidence and grayscale contrast at crack edges, an enhanced edge map of crack is proposed to measure the width of each crack segment by translating the skeleton towards both edges. A large number of experiments taken on the synthetic and real-world pavement images demonstrate that the proposed method can accurately and robustly quantify various cracks with the average accuracy of 93.7% for crack width. It is promising for quantitative pavement condition assessment and maintenance.

Research on Relationships among Different Distress Types of Asphalt Pavements with Semi‐Rigid Bases in China Using Association Rule Mining: A Statistical Point of View

Distress types are significant for asphalt pavement maintenance decision, and relationships among them can greatly influence the decision outcomes. In this study, to analyze the relationships among different distress types from a statistical point of view, 282 asphalt pavements with semirigid base structures in 23 regions of China were surveyed to identify 12 distress types, which were subsequently investigated by association rule mining. Results show that the distress types can be categorized into independent distress types (IDDTs), dependent distress types (DDTs), and rutting secondary distress types (RSDTs) based on the relationships. The relationships among IDDTs are the strongest, and those between IDDTs and DDTs, between rutting and RSDTs, and among depression, pumping, and raveling are also strong, while the others are weak. The weak relationships should be ignored, while the strong ones should be considered to reduce cost and guarantee accuracy of maintenance decision. The IDDTs are the most important distress types, so preventing them from occurring or maintaining them immediately can greatly preserve good performance of asphalt pavements. To help analyze the relationships, a distress‐type system was established and verified. In conclusion, this work provides new insights to understand distress types.

Impact of environmental assessment and budgetary restrictions in pavement maintenance decisions: Application to an urban network

Road agencies are facing the challenges of aging pavements, deteriorating networks, and insufficient maintenance budgets. This study addresses two limitations in the current state of practice in pavement management. First, because the evaluation of maintenance strategies has traditionally focused on economic and technical aspects, it neglects the environmental impact of maintenance decisions. Second, current management systems often provide a unique, optimised pavement maintenance strategy based on a specific objective(s) and constraint(s). The main objective of the study is to analyse the effect of including environmental aspects and funding availability in the design of maintenance strategies. To achieve this objective, the study followed a three-step methodology. First, this study reviews existing practices on pavement maintenance and the criteria considered to trigger the application of maintenance treatments and their effects on pavement condition. Then, maintenance strategies are optimised considering three levels of budgetary capacity and a sustainable evaluation which incorporates technical, economic, and environmental aspects over the pavement lifecycle. Finally, a case study dealing with an urban pavement network in Chile is analysed. Results obtained from this case study show that an increment of 2% in maintenance budget allows to account for more sustainable maintenance decisions, such as cold in-place recycling and full-depth slab repair, whose greenhouse gas emissions are lower than other alternatives. Results also show that functional asphalt overlay and microsurfacing are more recommended in flexible pavements when budgetary restrictions are low, whereas recommended treatments for rigid pavements exhibit small variability with budgetary restrictions.

Evaluation of Pavement Preservation and Maintenance Activities at General Aviation Airports in Texas

General aviation (GA) airports play an important role in the national transportation system by accommodating emergencies and providing for agricultural, recreational, and other operations. GA airports account for almost 90% of the airports in the Texas Airport System Plan. To protect their initial investment in asphalt and concrete pavements, GA airports are expected to perform routine pavement maintenance. For this case study of pavement maintenance practices at GA airports in Texas, GA airport managers were contacted and interviewed about treatments used at their facilities and about the process of having the work done. Respondents were asked to rank the effectiveness of specific maintenance treatments for asphalt and concrete pavements on a Likert-type scale, as well as marking type, retexturing methods, and rubber and contaminant removal methods. Information about the level at which pavement maintenance decisions were made, the process, and the additional data desired (specifically about pavements) was obtained through open-ended questions. The results show that crack sealing and slurry sealing are the two most commonly used maintenance treatments—in a reactive manner. The results also indicate that most GA airport managers rely on the Aviation Division of the Texas Department of Transportation or an engineering firm to manage the airfield pavement and do not take an active approach to implementing routine preventive maintenance. This reliance was the result of two factors: (a) level of knowledge, of both the airport managers and those approving funding, and (b) availability of funding. Opportunities for cooperation and education to address deficiencies exist within the current system.

Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Transportation agencies devote significant resources toward the collection of highly detailed and accurate pavement roughness data by using profiler vans to support pavement maintenance decisions. However, these agencies often cannot afford to measure roughness annually for the whole pavement network. This study introduced an improved acceleration-based metric, an index normalized by vehicle operating speed, to be used on a regular basis to screen pavement segments that are likely to be deficient; then a profile van can be sent to accurately measure the roughness condition. A profile van collected pavement profile data on 50 mi (80 km) of roadway; these data were then used to calculate the international roughness index (IRI). Meanwhile, two tablet computers were placed on the vehicle floor to collect data, including three-way accelerations, GPS coordinates, and vehicle speeds. A normalized acceleration-based index was created by incorporating a speed factor. Furthermore, logistic regression models were created to evaluate the effectiveness of the proposed index in identifying deficient pavement sections [IRI ≥ 140 in./mi (2.21 m/km)]. The proposed acceleration-based metric was able to identify between 80% and 93% of all deficient pavement sections. This research points to the feasibility of using a cost-effective acceleration-based application for network screening, a process that will reduce the total mileage of pavement sections needing to be measured and meanwhile still identify locations for which maintenance work is necessary.

Pavement Maintenance Decision Support System with Web-GIS Integrated

In order to provide a effective pavement analyze and decision making platform for the highway engineers, a pavement maintenance decision support system with Web-GIS integrated is implemented in the system. In this work, format of the data is predefined as a specification of the system. All the data are organized according to their relationship. Google map service is used as Web-GIS platform. The system is implemented according to JavaEE framework. The components of the system are implemented as java classes. Pavement performance evaluation, prediction and maintenance strategy decision are implemented as corresponding services. They are integrated as a system. According to the results, the system is effective and reliable.

Application of GIS/GPS in Shanghai Airport Pavement Management System

Shanghai airport pavement management system (SHAPMS) has been developed and updated for the Hongqiao and Pudong international airports that utilize the power of GIS and GPS for data collection, data storage, geospatial analysis of pavement evaluation and optimization of maintenance planning. This paper discusses the function design and the application effort of using GIS and GPS in the system. The practice shows that the pavement inspection time is reduced greatly, the geospatial tools are available for maintenance and rehabilitation, and the reliable data can be provided to solve future problems and optimize pavement maintenance decisions.

Decision Support Models for Asset Management of Low-Volume Roads

Pavement maintenance decisions are based on skimpy rules and rules of thumb. Generally, the selection of roads for maintenance is based on a worst-first policy. The timing of maintenance intervention and standards adopted for maintenance govern the service life of the pavement. The rate of pavement deterioration depends on the timing of maintenance intervention, choice of the type of maintenance treatment, and the quality of the construction. In the present investigation, the periodic performance data of typical low-volume rural roads constructed under the rural road development programs in India are considered. Performance prediction models are developed. The models are used to predict the performance during the design life. Different maintenance interventions are considered at different pavement conditions. The effect of the timing of maintenance on agency costs and vehicle operation costs is quantified and presented.

Analysis of rigid pavement distresses on interstate highway using decision tree algorithms

This paper describes the application of two decision tree algorithms, Logistic Regression Trees with Unbiased Selection (LOTUS) and Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE), to identify prevailing distress types that affect pavement maintenance decision making. The maintenance history of the selected sections within Interstate highway I-90 in the State of Wisconsin is modeled to build decision trees. The historical field distress survey data include the types, quantities, severities, and locations of distresses. The primary objective of this paper is to present the decision tree algorithms that may be incorporated into the decision making process and help state highway agencies make rational decisions during the course of pavement management. The paper discusses the model development process and prevailing distress identification procedures employing the decision tree algorithms. A case study is conducted on another Interstate highway, I-43, with a view to furthering the analysis of the results from the two algorithms. These analysis models and the results will provide the state highway agencies and practitioners with a means to identify prevailing distress types in a specific location and facilitate making decisions on future maintenance practices.

A Decision Support System for Flexible Pavement Treatment Selection

The current decision–making process of the Thailand Department of Highways (DOH) for pavement treatment selection is based on the engineering judgment of DOH practitioners, which causes inconsistency and ineffectiveness in treatment selections. In order to address these problems, Thailand Pavement Maintenance Decision Support System (TPMDSS) is developed in this study to help provide an effective guideline for flexible pavement treatment selection. TPMDSS integrates both the database management systems (DBMS) and the treatment decision model. A treatment decision model employs the causebased strategy, which follows a logical progression by synthesizing relevant pavement–related data to help identify pavement problems and recommend feasible treatments. The main components of TPMDSS are: (1) TPMS inventory module, (2) field inspection module, (3) pavement section analysis module, (4) distress explanation facility, and (5) reporting module. The developed system is verified and validated against the experts’ judgment and actual test cases.