Systematic approach to pavement management on regional and municipal road networks

2015 ◽  
pp. 151-156 ◽  
Keyword(s):  
Systematic Approach ◽  
Road Networks ◽  
Pavement Management

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

2021 ◽  
Vol 11 (6) ◽  
pp. 2458
Author(s):  
Ronald Roberts ◽  
Laura Inzerillo ◽  
Gaetano Di Mino
Keyword(s):  
Deep Learning ◽  
Management Practices ◽  
Prediction Models ◽  
Road Networks ◽  
Pavement Management ◽  
Efficient Operation ◽  
Pavement Maintenance ◽  
Goods And Services ◽  
Maintenance Decisions ◽  
Computational Systems

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.

scholarly journals Sustainable Maintenance of low level road network

2018 ◽  
Vol 196 ◽  
pp. 04063
Author(s):  
Jan Mikolaj ◽  
L’uboš Remek
Keyword(s):  
Network Management ◽  
Management System ◽  
Road Network ◽  
Road Networks ◽  
Pavement Management ◽  
Network Management System ◽  
Simple Method ◽  
Low Level ◽  
Technical Requirements ◽  
Local Road

Main goal of Road Network Management System is to ensure safety and continuity of road traffic on road network with low intensity and lower technical requirements. This is achieved with pavement management system (main component of road network management system). Most countries developed custom Pavement management systems (PMS) based on deterministic or probabilistic approach. Local road administrators of low level road networks often lack the software equipment such as HDM-4, RoSy, Exor, etc. These and similar PMS Most PMS, however effective, are often cumbersome, demanding in regard to energy, know-how and software equipment. The majority of local road administrators of rural road networks thus resort to non-effective reactive maintenance strategies. This article describes an easy to use method, based on predetermined maintenance repair & rehabilitation standards. Secondly, a simple method, based on road user cost, is introduced that administrator can use to prepare a list of road section eligible for repair according to their repair priority.

scholarly journals Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 319 ◽  
Author(s):  
Ronald Roberts ◽  
Gaspare Giancontieri ◽  
Laura Inzerillo ◽  
Gaetano Di Mino
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Road Networks ◽  
Integrated Approach ◽  
Pavement Management ◽  
Management Systems ◽  
Hotspot Analysis ◽  
Pavement Distress ◽  
Industry Standards ◽  
Pavement Management Systems

Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to continuously monitor the structural health of the pavement network. The methodology is applied to a road network in Sicily, Italy where there are numerous roads in need of rehabilitation and repair. Damage detection models were created which accurately highlight the location and a severity assessment. Harmonized distress categories, based on industry standards, are utilized to create practical workflows. This creates a pipeline for future applications of automated pavement distress classification and a platform for an integrated approach towards optimizing urban pavement management systems.

Reading Longer Words: Insights Into Multisyllabic Word Reading

2017 ◽  
Vol 2 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Lindsay Heggie ◽  
Lesly Wade-Woolley
Keyword(s):  
Systematic Approach ◽  
Word Reading ◽  
Explicit Instruction ◽  
Working Memory Capacity ◽  
Reading Difficulties ◽  
Instructional Programs ◽  
Morphological Knowledge ◽  
Morphological Complexity ◽  
Reading Accuracy ◽  
Grade 3

Students with persistent reading difficulties are often especially challenged by multisyllabic words; they tend to have neither a systematic approach for reading these words nor the confidence to persevere (Archer, Gleason, & Vachon, 2003; Carlisle & Katz, 2006; Moats, 1998). This challenge is magnified by the fact that the vast majority of English words are multisyllabic and constitute an increasingly large proportion of the words in elementary school texts beginning as early as grade 3 (Hiebert, Martin, & Menon, 2005; Kerns et al., 2016). Multisyllabic words are more difficult to read simply because they are long, posing challenges for working memory capacity. In addition, syllable boundaries, word stress, vowel pronunciation ambiguities, less predictable grapheme-phoneme correspondences, and morphological complexity all contribute to long words' difficulty. Research suggests that explicit instruction in both syllabification and morphological knowledge improve poor readers' multisyllabic word reading accuracy; several examples of instructional programs involving one or both of these elements are provided.

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