A challenge in infrastructure asset management occu rred in both developed countries and developing countries. The challenge is how to p redict infrastructure performance with appropriate models regarding the ability of co mplete data in order to make optimal maintenance and repair strategies. A great study on maintenance, repair and rehabilitation is generally in form of stochastic c ontrol models with found on Markov decision process (MDP). Standard models of infrastr ucture management are based on the MDP which the condition state of facilities is defined in discrete states. Statistical estimation methods have been applied to overcome th e problem of deterioration using econometric analysis approach. As a part of s tatistical method, the Markov hazard model becomes a valuable research that is pr oved by a plentiful paper and research produced in this field. Inspection data is n essential in Markov hazard models application. Accuracy of estimation occupied at least two inspection times which will be affected by quality of inspection dat a. A measurement error should be done due to errors occurred in inspection data as c au ed by measurement system or inspector (human or machine), inspected objects, or from data processing and data interpretation. Methods with focused on formulating evaluation techniques for quantifying the error term have been posted. In add ition, estimation methodologies using Bayesian estimation technique suggested in or der to overcoming small sampling population of inspection data and measurem ent errors has already documented. Recently, a variety of databases have been develope d by road administrators. However, it is still measly database functions cont ribu ing to infrastructure asset management. This fact mentions that even if a datab ase contains plentiful and detailed data, it is not a guarantee that it would definitely be used. Appropriate management suitable in developing countries depends o their needs and limitations. Poor systems of database or due to the database is se med like just black boxes are kind of challenge in database application in infras tructure asset management. Thus, a comprehensive strategy to build a methodology of database application in infrastructure asset management should be considere d. Understanding of basic knowledge of deterioration model in connecting with tools will be valuable in field of database development. This study is aimed at understanding and elaboratin g deterioration prediction models by referring to application of Markovian deteriorat ion hazard process for infrastructure asset management. Practically, focus es on the pavement asset management systems. Secondly is verifying and apply ing The Kyoto model Pavement Management Systems as a part of road pavem ent asset management systems with preferential on pavement database to s upport pavement maintenance work. Lastly is developing a practical approach of empirical studies of pavement maintenance management systems. The practical model is provided as open systems of database for practical orientation. Chapter 3 has discussed a methodology of maintenance managem ent with strategy and policy approach. The maintenance management pol icy is developed from viewpoint of mid/long-term maintenance plan. This p olicy is working with highway pavement maintenance. In order to verify the applic ability of this policy, an empirical study was conducted on maintenance manage ment in Kyoto City road pavement. This study has made a contribution to the field by comparison and benchmarking repair using maintenance policy approa ch. The maintenance policy approach represented can be extended to apply not o nly for main road pavement but to various other kinds of road facilities as well. In chapter 4, deterioration risk in pavement section was evalua ted by paying attention to road pavement maintenance issues, gene rat s routine maintenance work using statistical data, and proposes concept of ass et management system that continuously decreased deterioration risk. In addit ion, a methodology that requests maintenance strategy to aim at achieving reduction c st and making long-lived pavement was developed, and an approach that attemp ts in applying to actual maintenance work was considered. In that case, focu ses on maintenance of road pavement that local government managed, constructs pavement logic model for highway, and proposes operation method. In addition , management system to perform steadily such as repair method selection an d updating necessary data in order to propose benchmarking evaluation method at deterioration speed using statistical deterioration prediction model as a qua ntit tive evaluation method to achieve cost reduction and pavement long-lived. Chapter 5 has explained logic model and benchmarking evaluat ion that forms as basis of the Kyoto model more in detail, has discus ed a basic concept of Kyoto model pavement management system that has evaluatio n function and customization which enabled to overseas standardization, and has satisfied requirement of ISO for road pavement based on trend concerning internation al standardization of asset management. Furthermore, example of benchmarking evaluation was shown, and evaluation method that synchronized with logic model was confi rmed. The Kyoto model wish to emphasize overall platform of PMS composed by logic model in more with database, application and evaluation system, mechanism, stand ard, in order to construct mechanism of PMS in average not merely aimed at sta ndard application of PMS as pointed out but also while corresponding to each mo dule and AM plan process. In addition, to form the strategic pavement management system in a long-term perspective, to construct PDCA cycle, and to synchr onize with inspection function of ISO. When restructure asset management plan through Check and Action by ISO, the logic model is redefined. As a result, customizatio n of asset management system is ensured, and standardization of PMS that has divers ity is achieved. In chapter 6, it has proposed a practical model approach for un derstanding of pavement management system in order to solve of pro blem related to lack of information of pavement condition due to minimum ac tion of maintenance and inspection. Within this practical model, integrated database as a unification of several otherwise distinct files was an approach to sharing data and also enforcing standards to reach that goal. Having common open-source data is prominent effort in providing beneficial work purposes, for instance, in research , in academic, and in noncommercial works. Development a practical model of PMS is the way of handling problem of opensource pavement inventory data. In this works, hypo thetical approach is utilized. Virtual or imaginary but closes-in-reality as a poi nt of view with the target of highway network in form of city (municipalities) or province (prefectures) level target. Following integrated pavement database form s as customized database by flexible structure in regard to user’s options, and compatibility with external and internal models.
[1]
D. Vere-Jones.
Markov Chains
,
1972,
Nature.
[2]
Moshe E. Ben-Akiva,et al.
An Approach for Predicting Latent Infrastructure Facility Deterioration
,
1993,
Transp. Sci..
[3]
Adjo Amekudzi,et al.
Application of Cost Approach for Pavement Valuation and Asset Management
,
2002
.
[4]
Kiyoshi Kobayashi,et al.
A BENCHMARKING APPROACH TO PAVEMENT MANAGEMENT: LESSONS FROM VIETNAM
,
2009
.
[5]
W. G. Cochran.
Errors of Measurement in Statistics
,
1968
.
[6]
Eran Vigoda.
New Public Management
,
2007
.
[7]
Kiyoshi Kobayashi,et al.
Optimal Inspection and Replacement Policy using Stochastic Method for Deterioration Prediction
,
2007
.
[8]
Rod Stephens,et al.
Beginning Database Design Solutions
,
2008
.
[9]
James D. Hamilton.
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
,
1989
.
[10]
Kiyoshi Kobayashi,et al.
OPTIMAL REPAIR STRATEGIES WITH REFERENCE TO ECONOMIC LIFE EXPECTANCY
,
2004
.
[11]
Samer Madanat,et al.
Adaptive Optimization and Systematic Probing of Infrastructure System Maintenance Policies under Model Uncertainty - eScholarship
,
2006
.
[12]
Samy Noureldin,et al.
Asset Management Overview
,
2008
.
[13]
Kazuya Aoki,et al.
Measuring Deterioration Risk of Infrastructure
,
2008
.
[14]
Kiyoyuki Kaito,et al.
ROAD PATROL FREQUENCY AND HAZARDS GENERATION RISKS
,
2007
.
[15]
Dan M. Frangopol,et al.
Probabilistic models for life‐cycle performance of deteriorating structures: review and future directions
,
2004
.
[16]
Rabi G. Mishalani,et al.
Uniform Infrastructure Fields: Definition and Identification
,
1995
.
[17]
Kiyoshi Kobayashi,et al.
BRIDGE MANAGEMENT SYSTEM APPLICATION
,
2005
.
[18]
Walter L. Smith.
Probability and Statistics
,
1959,
Nature.
[19]
Frank E. Grubbs,et al.
Errors of Measurement, Precision, Accuracy and the Statistical Comparison of Measuring Instruments
,
1973
.
[20]
Samer Madanat,et al.
Optimal Inspection and Maintenance Policies for Infrastructure Networks
,
2000
.
[21]
Kiyoshi Kobayashi,et al.
DISAGGREGATED HAZARD RATES EVALUATION AND BENCH-MARKING
,
2008
.
[22]
Kiyoshi Kobayashi,et al.
PERSPECTIVES AND RESEARCH AGENDAS OF INFRASTRUCTURE MANAGEMENT
,
2003
.
[23]
Jorge A Prozzi,et al.
Estimation of Pavement Performance Deterioration Using Bayesian Approach
,
2006
.
[24]
António Pais Antunes,et al.
A Segment-linked Optimization Model for Deterministic Pavement Management Systems
,
2002
.
[25]
Dulcy M. Abraham,et al.
Estimating Transition Probabilities in Markov Chain-Based Deterioration Models for Management of Wastewater Systems
,
2006
.
[26]
Kiyoshi Kobayashi,et al.
Local mixture hazard model: A semi-parametric approach to risk management in pavement system
,
2008,
2008 IEEE International Conference on Systems, Man and Cybernetics.
[27]
J. Tobin.
Estimation of Relationships for Limited Dependent Variables
,
1958
.
[28]
Kiyoyuki Kaito,et al.
ESTIMATING MARKOVIAN TRANSITION PROBABILITIES FOR BRIDGE DETERIORATION FORECASTING
,
2005
.
[29]
Kiyoshi Kobayashi,et al.
AN OPTIMAL INSPECTION/REHABILITATION MODEL OF MULTI-COMPONENTS SYSTEMS WITH TIME-DEPENDENT DETRIORATION PROCESSES
,
2006
.
[30]
M. Y. Shahin,et al.
Pavement Management for Airports, Roads, and Parking Lots
,
2006
.
[31]
Ralph Haas,et al.
Infrastructure Management: Integrating Design, Construction, Maintenance, Rehabilitation and Renovation
,
1997
.
[32]
Frannie Humplick,et al.
HIGHWAY PAVEMENT DISTRESS EVALUATION: MODELING MEASUREMENT ERROR
,
1992
.
[33]
Kiyoyuki Kaito,et al.
A HIDDEN MARKOV DETERIORATION MODEL WITH MEASUREMENT ERRORS
,
2008
.
[34]
Kiyoshi Kobayashi,et al.
ROAD PAVEMENT MANAGEMENT ACCOUNTING SYSTEM APPLICATION
,
2004
.
[35]
Kiyoyuki Kaito,et al.
OPTIMAL MAINTENANCE STRATEGIES OF BRIDGE COMPONENTS WITH AN AVERAGE COST MINIMIZING PRINCIPLES
,
2005
.
[36]
Kiyoshi Kobayashi,et al.
A HYBRID GROUND CONSOLIDATION MODEL FOR AIRPORT PAVEMENT MANAGEMENT
,
2008
.
[37]
Kiyoyuki Kaito,et al.
BAYESIAN ESTIMATION OF MARKOV DETERIORATION HAZARD MODEL
,
2007
.
[38]
Samer Madanat,et al.
Optimization of Infrastructure Systems Maintenance and Improvement Policies
,
1999
.
[39]
Kiyoshi Kobayashi,et al.
DECENTRALIZED LIFE-CYCLE COST EVALUATION AND AGGREGATED EFFICIENCY
,
2005
.
[40]
Kiyoshi Kobayashi,et al.
ESTIMATING HAZARD MODELS FOR DETERIORATION FORECASTING
,
2005
.
[41]
William J. Stewart,et al.
Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling
,
2009
.
[42]
Samer Madanat,et al.
History-Dependent Bridge Deck Maintenance and Replacement Optimization with Markov Decision Processes
,
2007
.
[43]
Yoichiro Iwasaki,et al.
A PAVEMENT DETERIORATON FORCASTING MODEL WITH REFERENCE TO SAMPLE DROPPING
,
2007
.