Road Maintenance Costs Research Articles

The State of the Art on Phase Change Material-Modified Asphalt Pavement

During the construction and maintenance of asphalt pavement, a lot of non-renewable resources are consumed, which discharge a variety of waste gasses and smoke, causing a serious impact on the environment. Reducing society’s reliance on non-renewable resources is therefore key to improving sustainability. It is found that phase change materials (PCMs), as environmentally friendly materials, can spontaneously store and release heat energy by changing the phase state, thus reducing the adverse effect of temperature on asphalt pavement, reducing the occurrence of high-temperature stress, minimizing the cost of road construction and maintenance, and saving resources. In order to promote the application of PCMs in asphalt pavement, to promote self-controlling temperature technology for asphalt pavement, and to improve the sustainable development of asphalt pavement, this paper reviews the research status of PCMs in asphalt pavement, both domestically and abroad. The results show that the thermal conductivity of the modified asphalt binder can reach 0.29–0.39 W/mK, and the thermal diffusivity can reach 0.2–0.3 mm2/s, but the influence on the viscosity of the asphalt is limited, and both are less than 2000CP. The durability and thermal stability of the modified asphalt mixture are improved, and the maximum temperature can be lowered by 9 °C, which effectively reduces the occurrence of hightemperature stress. This review will help to better understand the function of PCMs and promote the sustainable development of green and environmentally friendly asphalt pavement.

Evaluation of road roughness and its influence on operating parameters in open-pit mines

Hauling ore and waste rock is a major cost factor in open-pit mines, encompassing both road maintenance and off-road truck operation. Road maintenance costs are directly influenced by traffic volume and the size of trucks using the roads. Off-road truck operating costs are impacted by various factors, including those affecting their movement, such as the direct interaction between tires and the road surface. Tire-road interaction significantly affects rolling resistance. Rougher surfaces with deeper grooves caused by track sinks lead to higher rolling resistance. This study employed laser profiling to characterize the surface roughness of five mine haul roads in two large Brazilian iron ore mines. This study evaluated the impact of these road irregularities on various operating parameters for 240-ton off-highway trucks, including rolling resistance, average travel speed, travel time, productivity, and unit transport cost. The results indicate that a 10 cm increase in road surface roughness can lead to a significant increase in rolling resistance (up to 5%), a substantial decrease in average speed (25%), a notable increase in travel time (26%), a decrease in productivity (19%), and a corresponding increase in unit transport cost (21%).

ROAD SAFETY AS FUNDAMENT IN ROAD INFRASTRUCTURE PLANNING

The road safety is a key indicator for the development of the society. May it be a key factor in the development of the road infrastructure? How will it affect the costs of road planning, construction, and maintenance? Will the “saved costs” from lowering the road accidents cover the higher cost of building and maintaining the roads? All those questions are difficult to answer; some seem even impossible. Nevertheless, it is important to analyse the problem, establish theoretical models, and formulate hypotheses. So we need to analyse the correlation between road safety, all the costs for road infrastructure, and the social effect in a theoretical manner. The paper aims to formulate the basis of the problem, and the potential solutions, analyse the needed data, and establish the theoretical models.

Optimization of Aggregate Composition of Asphalt Concrete Mixtures to Ensure Maximum Density

high strength and stability of asphalt concrete, but also at ensuring optimal characteristics of its structure.The main principles for the optimization of the grain composition are based on the parameters of the size and shape of thе stone aggregate particles included in the composition of asphalt concrete mixtures. An in-depth analysis of the grain composition helps to achieve an optimal balance between coarse and fine fractions, which in turn ensures the stability and durability of the asphalt concrete pavement. The maximum density of an asphalt concrete mixture plays a key role, influen-cing its physical and mechanical properties and ability to withstand aggressive environmental influences and traffic loads. In addition, the density of the mixture significantly affects its resistance to static and dynamic loads and deformations, which is important for ensuring road safety. The paper provides an analytical review of the basic principles and methodology for determining the grain composition of fine-grained asphaltic concrete mixtures with grain sizes up to 10 mm in order to achieve their maximum density. Both domestic and foreign methods for optimizing mixture design parameters are considered. Based on the consideration of ideal mathematical curves the grain composition, the main regularity for fine-grained asphalt concrete has been established, which has similar parameters to the mathematical Fourier curve. Understanding and practical application of the principles of constructing the grain composition of asphalt concrete mixtures of maximum density not only improves the quality of road construction, but also contributes to a more efficient use of resources, ensuring the stability and durability of the pavement. This leads to a reduction in road maintenance and repair costs, which is an important aspect in the context of the economic efficiency of infrastructure projects. Thus, improving the methods for investigating and controlling the grain composition of asphalt concrete is important for the sustainable development of road construction.

Investigating the cost of mechanized unpaved road maintenance operations in Uganda

Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture plantations. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.

Revealing the impact of losses on flexible pavement due to vehicle overloading

Pavement overload has become a significant concern in Indonesia due to its damaging effects on the pavement system, where most roads use flexible pavements. Meanwhile, overloading is also typical in other developing countries, causing everything from damage to accidents to the detriment of road users and operators. The current problem is that there is no definite knowledge of handling these overloaded vehicles, which has adverse effects. Therefore, this study aims to reveal the impact of losses caused by overloaded vehicles on flexible pavement. This research uses vehicle traffic load data to examine actual conditions in the field, which are converted to axle load values for each vehicle and used to determine pavement condition using the truck factor approach, mechanistic-empirical design, pavement smoothness level, and the impact of overloading due to fuel consumption by vehicles on flexible pavement. The results show that overloaded vehicles harm the flexible pavement. The increase in truck factor values for all vehicles in overloaded vehicles beyond the maximum allowable load results in more damage to the flexible pavement with an increase in truck factor values up to 83 %. The effect of overloading can be mitigated by increasing the thickness of the asphalt layer and the modulus of asphalt, so it is essential to pay attention to the quality of asphalt in anticipation of this overloading. To anticipate the overload phenomenon, it was found that the thickness of the asphalt overlay in the range of 170–205 mm could mitigate this detrimental effect. In addition, overloading affects the flexible pavement roughness and vehicle fuel consumption. Increased roughness and fuel consumption will increase road maintenance costs and affect driving comfort and safety. In addition, excessive fuel consumption can pollute the surrounding environment

Estimating cost of pothole repair from digital images using Stereo Vision and Artificial Neural Network

A significant amount of road maintenance cost goes into pothole repairs. The primary cost factors related to potholes are their size and depth, as larger and thicker potholes incur higher repair costs. However, existing methods for estimating pothole repair in developing countries rely on manual size measurements, which is time consuming, labor intensive, subjective and can lead to poor estimation of repair cost. This paper presents a system that can automatically determine the size of potholes from digital images and estimate the cost of repair. In this study, the stereo vision method was used to automatically estimate the depths of potholes from digital camera images. A feed-forward backward propagation Artificial Neural Network (ANN) was trained using pothole images acquired using mobile phones. The predicted depths and sizes of the potholes were then used to estimate the quantity of materials required to fill the potholes and subsequently, the cumulative cost of repair. Marking out and manual size measurements were performed for twenty randomly selected potholes in the Ugbowo Campus of the University of Benin, Nigeria. These measurements were compared against the estimated sizes of potholes predicted by the ANN model. A system was developed to automatically compute these material costs and considering other cost components such as transportation, labor, and equipment. Results obtained showed that the mean errors for depth, width and height estimates were 3.403%, 3.789% and 5.2617% respectively. Consequently, the developed system correctly estimated the cost of repair of the potholes considered in this study. A significant contribution of the paper is the speed and convenience of acquiring pothole data using a mobile phones without the need for on spot assessment of potholes or use of relatively more expensive stereoscopic camera setup.

LQR-Based Suspension for Heavy Vehicles Considering the Time-Varying Characteristics of Vehicle–Road Interaction

The rapidly increasing demand for heavy vehicles in the transportation sector has led to more severe road damage and significantly higher road maintenance costs. Investigation of active suspension system incorporating vehicle–road interaction (VRI) can provide effective means to reduce the road damage and improve the ride comfort of vehicles. In this paper, an active vehicle suspension controller based on the strongly stable and robust linear–quadratic regulator (LQR) principle is developed for the time-varying VRI system. The coupled system is modeled by a quarter-car traveling on the road modeled by an Euler–Bernoulli beam resting on a viscoelastic foundation. Two types of road irregularities, deterministic sine wave and random, are considered in the numerical studies. A time-frozen technique, by which the linear time-varying system is converted to a time sequence of time-invariant systems, is applied to solve for the coupled responses. Three variables, the dynamic tire load, vehicle body acceleration, and suspension relative displacement, are used to assess the effectiveness of the LQR-based controller in comparison to the passive suspension system. The performance of the active control is investigated for different vehicle speeds, vehicle loads, and road profiles. Numerical studies show that the controller can effectively reduce the road damage and improve the ride comfort, with a slight increase in the suspension relative displacement. This work lays a useful foundation in the problem formulation and system parameter influences for future vehicle suspension design and road damage mitigation including VRI.

МІЦНІСТЬ, ЗНОСОСТІЙКІСТЬ І МОРОЗОСТІЙКІСТЬ ФІБРОБЕТОНІВ ЖОРСТКИХ ДОРОЖНІХ ПОКРИТТІВ З БАЗАЛЬТОВОЮ ФІБРОЮ ТА ПОВІТРОВТЯГУЮЧОЮ ДОБАВКОЮ

Introduction. Increasing the durability of hard pavements allows you to reduce road maintenance costs by extending the intervals between repairs. Dispersed reinforcement is a well-known method of increasing the frost resistance and abrasion resistance of concrete, which has a positive effect on the durability of pavements in typical for Ukraine climatic conditions. Basalt fiber is resistant to corrosion and relatively inexpensive, which makes it promising for use in the road industry. Also, plasticizing and air-entraining admixtures must be used for concrete of hard pavement. Problem Statement. In modern economic conditions and taking into account the technological features of the preparation of mixtures, it is relevant to compare the effectiveness of the use of known methods of improving the properties of concrete for hard pavements: dispersed reinforcement and the use of air-entraining admixtures. Studying the expediency of the simultaneous use of these two methods of modifying the concrete mixture is also an important task from a scientific and practical point of view. Purpose. Determination of the influence of basalt fiber and air-entraining admixture on the strength, frost resistance and abrasion resistance of concrete of hard pavements.

Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition

AbstractTimely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non‐local (NL) block, which has the ability to efficiently capture long‐range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi‐scale context integration and long‐range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U‐Net, an effective crack segmentation model called CrackResU‐Net was developed. The test results on the existing CrackForest dataset showed that CrackResU‐Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state‐of‐the‐art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high‐precision recognition of pavement cracks for engineering purposes.

Field study toward anomaly road damage detection with drive recorder

As one of the ways to reduce road maintenance costs, road damage detection with a mobile camera is gaining attention. Most of conventional damage detection use supervised learning, nevertheless three practical drawbacks exist. Firstly, supervised learning requires a high manual cost to collect annotated data for training. Secondly, some damages are rarely observed, resulting in imbalanced data and difficulty in training an efficient model for all damage categories. Additionally, annotators may not identify such rare damages correctly. Thirdly, supervised learning cannot detect unknown categories of damages, though unknown categories are often found in a practical scene. To overcome these three drawbacks, we propose an ensemble model that combines anomaly detection and supervised damage detection. Anomaly detection can detect previously unknown and rare types of damage, while supervised damage detection ensures damages frequently observed on roads. Two different models cover wider categories of road damages. Our ensemble model is expected to achieve higher accuracy and lower manual cost.

Road Condition Monitoring Using Vehicle Built-in Cameras and GPS Sensors: A Deep Learning Approach

Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads’ conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles’ built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions.

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.

An Improved YOLOv5 Crack Detection Method Combined with a Bottleneck Transformer

Efficient detection of pavement cracks can effectively prevent traffic accidents and reduce road maintenance costs. In this paper, an improved YOLOv5 network combined with a Bottleneck Transformer is proposed for crack detection, called YOLOv5-CBoT. By combining the CNN and Transformer, YOLOv5-CBoT can better capture long-range dependencies to obtain more global information, so as to adapt to the long-span detection task of cracks. Moreover, the C2f module, which is proposed in the state-of-the-art object detection network YOLOv8, is introduced to further optimize the network by paralleling more gradient flow branches to obtain richer gradient information. The experimental results show that the improved YOLOv5 network has achieved competitive results on RDD2020 dataset, with fewer parameters and lower computational complexity but with higher accuracy and faster inference speed.

Study on Geosynthetics in Pavement Design

Geosynthetic reinforcement is one of the techniques adopted to improve their performance in pavement design.Geogrid are single line and multi-layer materials usually made by extruding and stretching high density polyethylene,or by weaving or knitting and coating high-strength polyester yarns.The resulting lattice structure has large holes that improve interaction with the soil aggregate. Roads having mostly the problems like the formation of potholes, ruts, cracks and localized depression and settlement, especially during rainy season. These are mainly due to the insufficient bearing capacity of the subgrade in water saturated condition.Geogrids are reinforcing elements to strengthen the ground in order to work on very soft and weak ground in the construction of asphalted or unpaved vehicle roads and areas where the high traffic flow is expected.Laboratory and simulated field CBR tests are conducted on soil samples with and without the inclusion of geogrid layer and also by varying the position of it in the mould. Use of geogrid increases the CBR value of the subgrade and thereby reduces the pavement thickness considerably up to 40%.This study will have positive impact as it will be reduce both project and road maintenance costs.

Pavement Distress Identification Based on Computer Vision and Controller Area Network (CAN) Sensor Models

Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments.

Bridging the user equilibrium and the system optimum in static traffic assignment: a review

AbstractSolving the road congestion problem is one of the most pressing issues in modern cities since it causes time wasting, pollution, higher industrial costs and huge road maintenance costs. Advances in ITS technologies and the advent of autonomous vehicles are changing mobility dramatically. They enable the implementation of a coordination mechanism, called coordinated traffic assignment, among the sat-nav devices aiming at assigning paths to drivers to eliminate congestion and to reduce the total travel time in traffic networks. Among possible congestion avoidance methods, coordinated traffic assignment is a valuable choice since it does not involve huge investments to expand the road network. Traffic assignments are traditionally devoted to two main perspectives on which the well-known Wardropian principles are inspired: the user equilibrium and the system optimum. User equilibrium is a user-driven traffic assignment in which each user chooses the most convenient path selfishly. It guarantees that fairness among users is respected since, when the equilibrium is reached, all users sharing the same origin and destination will experience the same travel time. The main drawback in a user equilibrium is that the system total travel time is not minimized and, hence, the so-called Price of Anarchy is paid. On the other hand, the system optimum is an efficient system-wide traffic assignment in which drivers are routed on the network in such a way the total travel time is minimized, but users might experience travel times that are higher than the other users travelling from the same origin to the same destination, affecting the compliance. Thus, drawbacks in implementing one of the two assignments can be overcome by hybridizing the two approaches, aiming at bridging users’ fairness to system-wide efficiency. In the last decades, a significant number of attempts have been done to bridge fairness among users and system efficiency in traffic assignments. The survey reviews the state-of-the-art of these trade-off approaches.

Assessment of Life-Cycle Benefits of Bio-Based Fog Sealant for Low-Volume Asphalt Pavement Preservation

Bituminous pavement can become brittle and distressed over time as a result of the oxidation of bitumen associated with exposure to air, and multiple preservation methods could be implemented to prevent oxidation and prolong the lifespans of asphalt roads. Fog seal, a common preservation strategy, refers to applying a thin layer of emulsion on an existing surface to mitigate oxidation and moisture penetration. Over recent decades, some innovative sealants derived from biomass have drawn considerable attention given their economic benefits and eco-friendly properties, including a proprietary soybean-derived sealer that has been successfully applied in many states. While some studies have investigated its performance and concluded that it could effectively preserve asphalt roads and potentially prolong their lifespans, there has been little comprehensive life-cycle cost analysis (LCCA) based on field performance and actual construction cost which would support appropriate recommendations to local public agencies. To fill this gap, this study selected a low-volume asphalt pavement in Clinton County, Iowa, and during summer 2016 sprayed a proprietary bio-based fog sealant on it for five consecutive years of investigation, including an annual distress survey conducted on both untreated and bio-based fog sealant-treated sections for comparison purposes. Field assessment during this five-year period indicated that bio-based fog seal treatment could control crack growth of asphalt-surfaced roads. Bio-based fog sealant installation and road maintenance costs were combined and LCCA was employed, with results suggesting that, among the various options investigated in this study, applying three treatments at five-year intervasl during a service period was the most cost-effective.

Study on the Mechanical Properties of Flexible Prefabricated Highway Structural Systems considering the Effect of Two-Way Fluid-Structure Interaction

The rainfall often causes changes in the physical performance of the embankment, which leads to disasters such as embankment collapse and road surface settlement and cracking. Past research has tended to be limited to traditional embankment seepage models and is mostly based on the assumption that surface precipitation is uniform and rainfall intensity is constant. To adapt to the changes and development of the times, based on the water leakage mode of the flexibly assembled highway embankment, a new rainfall calculation model of the reinforced soil embankment is constructed and combined with the solid-fluid coupling physics theory, the sedimentation amount and slope stability of the embankment are analyzed at multiple levels, the mechanical properties of the new embankment under rainfall conditions are summarized, the factors affecting the settlement of the embankment and the stability of the slope are explored, and the reference model is provided for the construction of the new embankment, and the existing construction process is improved through multilevel cause analysis. We improve and develop the existing theoretical system to reduce the probability of road diseases, improve the efficiency of road construction, and reduce the cost of road maintenance.

Analisis Sistem Pengambilan Keputusan Penentuan Konstruksi Jalan dengan Perkerasan Kaku atau Fleksibel (Studi Kasus Jalan di Propinsi Maluku Utara)

ABSTRACT
 This research discussing about decision support system analysis in determining concrete and composit road construction compared to asphalt construction for the case of roads in North Maluku Province. This study uses the Analytic Hierarchy Process (AHP) method or model as a method to analyze the feasibility of concrete and composit construction compared to asphalt construction. There are at least 7 (seven) factors that are considered as AHP inputs, namely road conditions, technical pavement, ecomonical/cost, duration/method of construction, availability of equipment, environmental conditions, and availability of materials. This study method includes data collection and analysis methods. For data collection, this study uses a questionnaire method that contains questions related to technical and non-technical factors that are used to assess the feasibility of a road for selecting a type of road pavement with flexible or rigid pavement. The construction feasibility of concrete and composite pavements is better than that of flexible asphalt pavements on road constructions, with the following sub-criteria conditions; roads that have bends and inclines, lower road maintenance costs, excellent weather and environmental resistance, less dense traffic, easy work execution and can be done at any time, do not require a lot of AMP and equipment, and asphalt availability can't fast time. The alternative of road pavement type is effective as a weight value of 33.78% with using the composite pavement construction.
 Keywords: Roadworthiness, Composite pavement, AHP, Decision Support System, North Maluku