Search Results for « Road Maintenance »

Of all public assets, road infrastructure tops the list. Roads are crucial for economic development and growth, providing access to education, health, and employment. The maintenance, repair, and upgrade of roads are therefore vital to road users' health and safety as well as to a well-functioning and prosperous modern economy. The EU-funded HERON project will develop an integrated automated system to adequately maintain road infrastructure. In turn, this will reduce accidents, lower maintenance costs, and increase road network capacity and efficiency. To coordinate maintenance works, the project will design an autonomous ground robotic vehicle that will be supported by autonomous drones. Sensors and scanners for 3D mapping will be used in addition to artificial intelligence toolkits to help coordinate road maintenance and upgrade workflows.

Roads, and means of transport, make a crucial contribution to economic development and growth and bring important social benefits. Poorly maintained roads constrain mobility, significantly raise vehicle operating costs, increase accident rates and their associated human and property costs, and aggravate isolation, poverty, poor health, and illiteracy in rural communities. This Note highlights the economic and social importance of regular road maintenance and recommends ways to achieve sustainable road maintenance with scarce public resources. Its audience is not specialists but rather people who need to understand road maintenance enough to discharge their responsibilities effectively: government policy-makers, mayors, ministry staff, new World Bank staff and staff in sectors such as rural development and social funds. The reference section offers sources providing more detailed information.

Road infrastructure maintenance inspection is typically a labour-intensive and critical task to ensure the safety of all the road users. In this work, we propose a detailed methodology to use state-of-the-art techniques in artificial intelligence and computer vision to automate a sizeable portion of the maintenance inspection subtasks and reduce the labour costs. The proposed methodology uses state-of-the-art computer vision techniques such as object detection and semantic segmentation to automate inspections on primary road structures such as the road surface, markings, barriers (guardrails) and traffic signs. The models are mostly trained on commercially viable datasets and augmented with proprietary data. We demonstrate that our AI models can not only automate and scale maintenance inspections on primary road structures but also result in higher recall compared to traditional manual inspections.

Abstract Several roads in Indonesia are in bad conditions. It is because maintenance works is not properly conducted because of insufficiently, unsustainably, and inconsistency on allocating budget for maintenance. Insufficient or uncertain budgetary allocations to road maintenance have resulted in road deterioration that has significantly increased production and transport costs. To avoid this problem, highway professionals advocate the establishment of dedicated road funds, managed by independent road boards made up of road user representatives. So far roads maintenance in Indonesia is financed using the government budget allocation. It is difficult to give guarantee to the sustainable road maintenance if the finance for road maintenance takes from the government budget allocation. Therefore in this paper will be introduced road fund, the road maintenance funding system widely used in some countries that road conditions can be well maintained. The conclusion is that the road fund needs to be applied to maintenance the road in Indonesia. Keywords: Road, deteriorate, maintenance, road fund, budget system, sustainable Abstrak Sebagian jalan di Indonesia dalam kondisi buruk. Hal ini disebabkan karena pekerjaan perbaikan tidak dilakukan dengan baik karena kekurangan, ketidak berkelanjutan, dan ketidak konsistensi dari alokasi dana untuk pemeliharaan jalan. Kekurangan dan ketidak jelasan alokasi pendanaan untuk perbaikan jalan menyebabkan memburuknya kondisi jalan yang menyebabkan peningkatan biaya produksi dan transportasi. Untuk menghindari masalah ini, professional pengembangan jalan raya didorong untuk menciptakan dana jalan secara khusus dan dikelola oleh badan independen jalan yang terdiri dari pemangku kepentingan para pengguna jalan. Sejauh ini, pemeliharaan jalan di Indonesia dibiayai melalui alokasi dana pemerintah. Hal ini menyebabkan sulit untuk memberikan jaminan keberkelanjutan pemeliharaan jalan. Untuk ini, makalah ini mengintroduksi dana jalan untuk sistem pemeliharaan jalan yang umum digunakan di beberapa Negara yang memiliki jalan dalam kondisi terpelihara dengan baik. Kesimpulan adalah dana jalan diperlukan untuk diterapkan untuk pemeliharaan jalan di Indonesia. Kata-kata kunci: Kerusakan jalan, pemeliharaan, dana jalan, sistem pendanaan, berkelanjutan

The high costs for the management of the modern and complex industrial control systems make it necessary to enhance the current maintenance processes. Therefore, the need arises to clearly define the goals of the maintenance, in order to evolve and continuously enhance the management methods, to efficiently integrate the maintenance activities with the ones related to the production, the service provisioning, and the operation, and to use smart computer-based maintenance systems. This paper proposes a general maintenance approach and its specific application to the railway sector.

In Africa, roads are the dominant mode of passenger and freight transport, for which the need is growing rapidly. It is noticeable that most of the African countries do not do enough to ensure the sustainability of road infrastructure as it has been widely reported that roads are affected, to varying degrees, by premature deterioration. Most of the African countries have adopted institutional reforms, notably entailing the creation of road funds and road agencies, and made significant progress on road maintenance. However, many challenges remain to be addressed in all of them to ensure appropriate maintenance. Although spending on road maintenance has increased over time in all African countries it remains insufficient to cover the needs. Poorly maintained roads constrain mobility, significantly raise vehicle operating costs, increase accident rates and their associated human and property costs, and aggravate isolation, poverty, poor health, and illiteracy in rural communities. This paper focuses, in particular, on road maintenance in some African countries considering types of road maintenance and the different approaches aiming at a comparison to reflect on similarities and differences.

Due to limited budgets allocated for road maintenance projects in various countries, road management departments face difficulties in making scientific maintenance decisions. This paper aims to provide road management departments with more scientific decision tools and evidence. The framework proposed in this paper mainly has the following four innovative points: 1) Predicting pavement performance deterioration levels of road sections as decision basis rather than accurately predicting specific indicator values; 2) Determining maintenance route priorities based on multiple factors; 3) Making maintenance plan decisions by establishing deep reinforcement learning models to formulate predictive strategies based on past maintenance performance evaluations, while considering both technical and management indicators; 4) Determining repair section priorities according to actual and suggested repair effects. By resolving these four issues, the framework can make intelligent decisions regarding optimal maintenance plans and sections, taking into account limited funds and historical maintenance management experiences.

The application of multimodal data in road maintenance has attracted considerable attention due to its potential to enhance decision-making processes and improve infrastructure resilience. This paper provides a comprehensive review of the utilisation of various modalities of multimodal data, including LiDAR, RGB images, thermal images, ground-penetrating radar (GPR), text, audio, and some others for road maintenance tasks. The research methodology thoroughly examines existing literature, categorising data modalities and analysing their respective applications. The paper discusses the integration and fusion of multimodal data, spatial and temporal analysis techniques, decision support systems, strategies for resilience and adaptability and information requirements in for road maintenance. It also explores data structures for integration into digital twin, advanced methodologies for sensor fusion, integration of new sensors and data types and multimodal sensors into road maintenance. This comprehensive review underscores the significance of multimodal data in enhancing the efficiency and effectiveness of road maintenance activities and identifies gaps in the automatic fusion of different modalities in the context of road asset management.

The goal of predictive maintenance is to forecast the occurrence of faults of an appliance, in order to proactively take the necessary actions to ensure its availability. In many application scenarios, predictive maintenance is applied to a set of homogeneous appliances. In this paper, we firstly review taxonomies and main methodologies currently used for condition-based maintenance; secondly, we argue that the mutual dissimilarities of the behaviours of all appliances of this set (the "cohort") can be exploited to detect upcoming faults. Specifically, inspired by dissimilarity-based representations, we propose a novel machine learning approach based on the analysis of concurrent mutual differences of the measurements coming from the cohort. We evaluate our method over one year of historical data from a cohort of 17 HVAC (Heating, Ventilation and Air Conditioning) systems installed in an Italian hospital. We show that certain kinds of faults can be foreseen with an accuracy, measured in terms of area under the ROC curve, as high as 0.96.

Understanding the process of stakeholder satisfaction is a prerequisite for successful stakeholder management. The expectancy disconfirmation model describes the process of stakeholder satisfaction by relating customers’ satisfaction with a product or service to discrepancy between the perceived performance of and the prior expectations about the product or service. Although a few authors have applied the model to investigate citizens’ overall satisfaction with local government services, little research has been undertaken using the model to examine satisfaction with public-sector projects. This paper addresses the relationship between stakeholder expectation and satisfaction in road maintenance. The expectancy disconfirmation model was used to investigate the expectations stakeholders had about the influence of road maintenance on the performance of the road during and after maintenance. Data were gathered on stakeholders’ expectations of a maintenance project before the project started, and on stakeholders’ perception of and the overall satisfaction with the project after the project was finished. The paper pays particular attention to the influence of the disconfirmation of expectations on the satisfaction with the maintenance project. Based on that, conclusions are drawn for managing stakeholders’ expectations in road maintenance.