APPLICATION OF ADVANCED COMPUTATION ON FLEXIBLE PAVEMENT MAINTENANCE MANAGEMENT SYSTEM IN TAIWAN Jia-Ruey Chang 1 Ching-Tsung Hung 2 Jyh-Dong Lin 3 1
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Assistant Professor, Department of Civil Engineering, MingHsin University of Science & Technology, No. 1 Hsin-Hsing Road, Hsin-Fong, Hsin-Chu 304, Taiwan. Tel: 886-3-5593142 ext. 3295; Fax: 886-3-5573718; E-mail:
[email protected] Ph.D. student, Department of Civil Engineering, National Central University, Taoyuan, Chungli, 32054, Taiwan. Tel: 886-3-4269270; Fax: 886-3-4227183; Email:
[email protected] Professor, Department of Civil Engineering, National Central University, Taoyuan, Chungli, 32054, Taiwan. Tel: 886-3-4220338; Fax: 886-3-4227183; Email:
[email protected]
Keywords: Flexible Pavement, Pavement Maintenance Management System (PMMS), Present Serviceability Index (PSI), Expert System (ES), Fuzzy Analysis, Multiple Criteria Decision Making (MCDM), Geographic Information System (GIS), Soft Computation Introduction Pavement management is not a new concept and pavement maintenance can significantly improve pavement’s performance. Authorizations in Taiwan have been faced with decreasing budgets of maintenance and increasing road lengths and transportation needs. For the purpose of improving the efficiency of maintenance and management, several advanced computations were introduced and integrated into Pavement Maintenance Management System (PMMS) in the study. The main objective is to establish a practical and localized network-level PMMS based on Geographic Information System (GIS) for flexible pavement under the consideration of actual situations in Taiwan [1]. Establishment of Present Serviceability Index (PSI) Model The present serviceability on specific pavement sections in Chung-Li Engineering Section of Taiwan Highway Bureau (THB) were collected periodically by THB’s ARAN (Automated Road ANalyzer) and stored to establish an analytical database for sequential tasks. Present Serviceability Index (PSI) was established to determine the quality of pavement. Present serviceability rating (PSR) was surveyed from panel rating, and rutting, roughness, and various distress data were collected by ARAN. Considering the interaction among factors which affect pavement performance, fuzzy integral method was used to establish the non-interactive integrated value [2]. Based on both subjective PSR and objective ARAN data, PSI was developed by fuzzy integral incorporated with fuzzy regression. Because the conventional regression model cannot deal with the fuzzy characteristic existed in PSR, fuzzy regression model was used to establish the PSI relationship between subjective rating (PSR) and objective collection (by ARAN). Fuzzy integral not only eliminates the shortcomings of conventional additive measure, but also improve the accuracy of fuzzy regression. The predictive ability of this PSI model is 93.32% and the critical PSI value is 2.19 [3].
Copyright 2005 by International Road Federation
Establishment of Structural Strength Index (SSI) Model THB’s FWD (Falling Weight Deflectometer) (JILS-20) was utilized on specific sections. A temperature correction equation was developed and a structural evaluation system, Structural Strength Index (SSI), was established for rapid and reliable assessment of pavement structural conditions [4]. The SSI evaluation system can assist transportation authorities in prioritizing routine preventive maintenance and rehabilitation needs. Temperature correction factors were established based on the 1176 FWD tests on two specific built test sections. Thus, the effects of traffic load on pavement sections can be eliminated. Comparisons of temperature correction factors with other studies were performed. It is interesting to find that although in those studies temperature correction factors for deflection were developed under different climatic conditions and pavement structures, the temperature correction factors differ, in average, by only 13%. Three significant indicators for upper and subgrade layers were identified through the comprehensive statistical analyses. Criteria were established so that a pavement engineer can repeatedly use those three indicators for cross checking and verifying the most reasonable assessment. For overall structural evaluation, SSI was established, with criteria to differentiate good or poor pavement structural condition. One of the advantages of using the SSI is that after the criteria are established, no backcalculation process is required to determine whether structural conditions are good or poor. The SSI is a direct computation from the FWD deflection measurements which are first corrected for temperature. As more experience is gained using the SSI evaluation system, a revised system will provide higher reliability and accuracy. Expert System for M&R Strategy Based on Fuzzy Theory Knowledge acquisition is one of the most important factors in producing an expert system. The first step in developing the M&R (Maintenance & Rehabilitation) strategy expert system was acquiring knowledge from literatures reviews and 33 experienced pavement experts through several panel meetings and discussions to establish one comprehensive and robust knowledge database. The knowledge base of this expert system was composed of a reasoning path and a calculation path. The experts’ knowledge was used to create the flow of reasoning path for both M&R strategy selection and cost estimation as a main decision path, and the calculation path was a supporting system which based on pavement distress types, severity, quantity and IRI required in the reasoning path using fuzzy theory. The concepts of fuzzy relation and fuzzy composition were utilized to illustrate the vague relationship between distresses and required M&R strategies. Then, the decision trees (reasoning rules) for each distress type were derived. One user-friendly expert system that embodies experts’ knowledge, experience, and subjective judgment was developed to provide pavement engineers with an educational aid as well as an interactive tool for evaluating pavement conditions, selecting the appropriate M&R strategies, and estimating their corresponding costs. Multiple Criteria Decision Making (MCDM) Methods for Priority Model In general, decisions are often based on a subjective viewpoint rather than an objective technical criterion. Multiple Criteria Decision-Making (MCDM) methods can be considered to be very suitable for solving practical complex problems [5] such as the prioritization of maintenance/rehabilitation works for overall road network. In the study, one priority model for 39 pavement sections was developed based on five evaluators - PSI, SSI, traffic volume, heavy-truck ratio, and M&R cost [1]. Four MCDM methods - AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), ELECTRE (ELimination Et Choice Translating REality), GRA (Grey Relation Analysis) - were performed. The weights of each evaluator were obtained by means of 13 effective questionnaires. Various combinations of the five evaluators were used to perform MCDM
analyses for cross checking and verifying the most reasonable assessment. From the pairwise comparisons among these four methods, they cannot differentiate from each other easily and their trends shown are similar. Different analytical methods should be used according to actual application and decision maker’s preferences. We found that the evaluator “heavy-truck ratio” is more important and suitable than “traffic volume” in Taiwan. Therefore, PSI, SSI, heavy-truck ratio, and M&R cost are the four appropriate evaluators for prioritization in Taiwan. Establishment of PMMS Based on GIS GIS uses a coordinate system to define the location of each feature of the pavement network. It is a strong visual aid for the representation of both present and future pavement conditions [6]. According to the above efforts, MapInfo was adopted as the GIS developmental tool to design system architecture, integrate data formats, import databases, and code all models in sequence. One PMMS–GIS suitable for Taiwan was established. Combining road maps with all relative information in this system, not only pavement engineers can monitor pavement condition at anytime, but also maintenance budgets can be estimated. PMMS–GIS provide authorities as a reference for advanced exploration and can certainly enhance the efficiency and quality of pavement maintenance by means of advanced computations. Acknowledgements The authors would like to express their sincere appreciations to Taiwan Highway Bureau (THB) for their assistances on providing resources and equipments. References 1. Chang, J. R. (2001). “Establishment of Network-Level Flexible Pavement Maintenance Management System in Taiwan – The Case Study on Chung-Li Engineering Section of Taiwan Highway Bureau,” Ph.D. Dissertation, Graduate Institute of Civil Engineering, National Central University, Chungli, Taiwan. (in Chinese) 2. Sugeno, M. (1974). “Theory of fuzzy integrals and its application,” Ph.D. Dissertation, Tokyo Institute of Technology. 3. Chang, J. R., Tzeng, G. H., Hung, C. T., and Lin, H. H. (2003). “Non-Additive Fuzzy Regression Applied to Establish Flexible Pavement Present Serviceability Index,” The IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2003), St. Louis, MO, U.S., pp. 1020-1025. 4. Chang, J. R., Lin, J. D., Chung, W. C., and Chen, D. H. (2002). “The Structural Strength of Flexible Pavements in Taiwan Evaluated by Falling Weight Deflectometer,” International Journal of Pavement Engineering, Vol. 3, No. 3, pp. 131-141. 5. Duckstein, L., and Opricovic, S. (1980). “Multiobjective optimization in river basin development,” Water Resources Research 16(1), pp. 14-20. 6. Simkowitz, H. J. (1990). “Using Geographic Information System Technology to Enhance the Pavement Management Process,” Transportation Research Record 1593, Highway and Facilities Design, Geographic Information Systems, pp. 10-19.
Figure 1 System architecture
Figure 2 Query subsystem – management area
Figure 3 Query subsystem – traffic station
Figure 4 Query subsystem – historical M&R activities
Figure 5 Query subsystem – specific road
Figure 6 Query subsystem – ARAN & FWD data (by text)
Figure 7 Query subsystem – ARAN data (by graph)
Figure 8 Query subsystem – FWD data (by graph)
Figure 9 Analytical subsystem – PSI
Figure 10 Analytical subsystem – SSI
Figure 11 Pavement image captured from ARAN
Figure 12 Expert subsystem for M&R strategy
Figure 13 Suggestion for M&R strategy and its cost
Figure 14 PSI for overall pavement network
Figure 15 Priority for each section in pavement network
