Using Decision Tree for Data Mining of Pavement Maintenance and Management

2013 ◽  
Vol 330 ◽  
pp. 1015-1019 ◽  
Author(s):  
Jyh Dong Lin ◽  
Wei Hsing Huang ◽  
Chia Tse Hung ◽  
Chien Ta Chen ◽  
Jih Chiang Lee
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Management System ◽  
Measurement Data ◽  
Milling Process ◽  
Pavement Management ◽  
Data Mining Algorithm ◽  
Pavement Management System ◽  
Pavement Maintenance ◽  
System Integrity

In recent years advancements in the Information Technology (IT), have enabled automation of pavement measurement data. A large amount of data can be saved for a pavement management system. The study of pavement maintenance and management has include many methods, such as expert system, decision support analysis and data mining (DM) . In this study we use decision tree for data mining algorithm C5.0 has been used in this analysis. After acceptance of the decision tree, we make use of algorithms and computing for classification. This method is used to check the pavement management system database and make a comparison of all data. The result shown a correct classification of about 61% its still improved space. According to this result we discuss three analysis results included: 1.Database information is correct or not 2.Road pavement never homogenization 3.Milling process never remove human factor. Finally useful pavement information and ways can improve system integrity and correctly.

scholarly journals Low-Cost Pavement Management System for Developing Countries

2021 ◽  
Vol 13 (11) ◽  
pp. 5941
Author(s):  
Shabir Hussain Khahro ◽  
Zubair Ahmed Memon ◽  
Lillian Gungat ◽  
Muhamad Razuhanafi Mat Yazid ◽  
Abdur Rahim ◽  
...  
Keyword(s):  
Developing Countries ◽  
Management System ◽  
Low Cost ◽  
Pavement Management ◽  
Qualitative Assessment ◽  
Pavement Management System ◽  
Pavement Maintenance ◽  
Sensitivity Assessment ◽  
Network Process ◽  
Fishbone Diagram

Governments face numerous challenges in sustaining road network conditions. This is attributed to road authorities’ shortages of financial and physical infrastructure. As a result, low-cost automated solutions are being pursued to solve these problems and provide people with appropriate road conditions. Several attempts have been made to improve these technologies and incorporate them into a Pavement Management System (PMS) but limited attempts are made for developing countries. This study aimed to design a low-cost pavement management system for flexible pavement maintenance. A detailed literature review has been carried out, followed by a qualitative assessment of the various indicators considered for PMS. The priority ranks of the PMS indicators were made using an Analytical Network Process (ANP) and each rank was validated by a sensitivity assessment test using the Super Decision-Making tool. This paper also provides the conceptual framework for the low-cost PMS, followed by a fishbone diagram of the indicators and sub-indicators. It is concluded that an emergency maintenance plan with an ANP weight of (0.41) is one of the most significant plans for a low-cost PMS, followed by a routine with an ANP weight of (0.39) and periodic maintenance plans with a (0.20) ANP weight. Moreover, the functional indicators with an ANP weight of (0.32) are the most significant indicators for a low-cost PMS, followed by structural (0.26), safety (0.24), and serviceability(0.18) indicators. This model will assist the road planners in making better decisions on pavement maintenance management plans. The model will suggest the pavement sections on a higher priority to be added in the maintenance plans, especially where the maintenance budget is limited.

scholarly journals Proposal and Implementation of a Heliport Pavement Management System: Technical and Economic Comparison of Maintenance Strategies

2021 ◽  
Vol 13 (16) ◽  
pp. 9201 ◽  
Author(s):  
Paola Di Mascio ◽  
Alessio Antonini ◽  
Piero Narciso ◽  
Antonio Greto ◽  
Marco Cipriani ◽  
...  
Keyword(s):  
Management System ◽  
Condition Index ◽  
Pavement Management ◽  
Pavement Management System ◽  
Pavement Condition ◽  
Airport Pavement ◽  
Maintenance And Rehabilitation ◽  
Pavement Condition Index ◽  
Rotary Wing ◽  
Economic Comparison

Maintenance and rehabilitation (M&R) scheduling for airport pavement is supported by the scientific literature, while a specific tool for heliport pavements lacks. A heliport pavement management system (HPMS) allows the infrastructure manager to obtain benefits in technical and economic terms, as well as safety and efficiency, during the analyzed period. Structure and rationale of the APSM could be replicated and simplified to implement a HPMS because movements of rotary-wing aircrafts have less complexity than fixed-wing ones and have lower mechanical effects on the pavement. In this study, an innovative pavement condition index-based HPMS has been proposed and implemented to rigid and flexible surfaces of the airport of Vergiate (province of Varese, Italy), and two twenty-year M&R plans have been developed, where the results from reactive and proactive approaches have been compared to identify the best strategy in terms of costs and pavement level of service. The result obtained shows that although the loads and traffic of rotary-wing aircrafts are limited, the adoption of PMS is also necessary in the heliport environment.

ANGEL Mining

2012 ◽  
pp. 94-115 ◽  
Author(s):  
Tyler Swanger ◽  
Kaitlyn Whitlock ◽  
Anthony Scime ◽  
Brendan P. Post
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Feature Selection ◽  
Decision Tree ◽  
Management System ◽  
Association Mining ◽  
Data Mining Techniques ◽  
Usage Patterns ◽  
Design Selection ◽  
Comprehensive College

This chapter data mines the usage patterns of the ANGEL Learning Management System (LMS) at a comprehensive college. The data includes counts of all the features ANGEL offers its users for the Fall and Spring semesters of the academic years beginning in 2007 and 2008. Data mining techniques are applied to evaluate which LMS features are used most commonly and most effectively by instructors and students. Classification produces a decision tree which predicts the courses that will use the ANGEL system based on course specific attributes. The dataset undergoes association mining to discover the usage of one feature’s effect on the usage of another set of features. Finally, clustering the data identifies messages and files as the features most commonly used. These results can be used by this institution, as well as similar institutions, for decision making concerning feature selection and overall usefulness of LMS design, selection and implementation.

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