A driving cycle is a velocity profile over time. Driving cycles can be used for environmental classification of cars and to evaluate vehicle performance. The benefit by using stochastic driving cycles instead of predefined driving cycles, i.e. the New European Driving Cycle, is for instance that the risk of cycle beating is reduced. Different methods to generate stochastic driving cycles based on real-world data have been used around the world, but the representativeness of the generated driving cycles has been difficult to ensure. The possibility to generate stochastic driving cycles that captures specific features from a set of real-world driving cycles is studied. Data from more than 500 real-world trips has been processed and categorized. The driving cycles are merged into several transition probability matrices (TPMs), where each element corresponds to a specific state defined by its velocity and acceleration. The TPMs are used with Markov chain theory to generate stochastic driving cycles. The driving cycles are validated using percentile limits on a set of characteristic variables, that are obtained from statistical analysis of real-world driving cycles. The distribution of the generated driving cycles is investigated and compared to real-world driving cycles distribution. The generated driving cycles proves to represent the original set of real-world driving cycles in terms of key variables determined through statistical analysis. Four different methods are used to determine which statistical variables that describes the features of the provided driving cycles. Two of the methods uses regression analysis. Hierarchical clustering of statistical variables is proposed as a third alternative, and the last method combines the cluster analysis with the regression analysis. The entire process is automated and a graphical user interface is developed in Matlab to facilitate the use of the software. / En körcykel är en beskriving av hur hastigheten för ett fordon ändras under en körning. Körcykler används bland annat till att miljöklassa bilar och för att utvärdera fordonsprestanda. Olika metoder för att generera stokastiska körcykler baserade på verklig data har använts runt om i världen, men det har varit svårt att efterlikna naturliga körcykler. Möjligheten att generera stokastiska körcykler som representerar en uppsättning naturliga körcykler studeras. Data från över 500 körcykler bearbetas och kategoriseras. Dessa används för att skapa överergångsmatriser där varje element motsvarar ett visst tillstånd, med hastighet och acceleration som tillståndsvariabler. Matrisen tillsammans med teorin om Markovkedjor används för att generera stokastiska körcykler. De genererade körcyklerna valideras med hjälp percentilgränser för ett antal karaktäristiska variabler som beräknats för de naturliga körcyklerna. Hastighets- och accelerationsfördelningen hos de genererade körcyklerna studeras och jämförs med de naturliga körcyklerna för att säkerställa att de är representativa. Statistiska egenskaper jämfördes och de genererade körcyklerna visade sig likna den ursprungliga uppsättningen körcykler. Fyra olika metoder används för att bestämma vilka statistiska variabler som beskriver de naturliga körcyklerna. Två av metoderna använder regressionsanalys. Hierarkisk klustring av statistiska variabler föreslås som ett tredje alternativ. Den sista metoden kombinerar klusteranalysen med regressionsanalysen. Hela processen är automatiserad och ett grafiskt användargränssnitt har utvecklats i Matlab för att underlätta användningen av programmet.

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Infrastructure systems are crucial facilities. They supply the necessary transportation, water and energy utilities for the public. However, while aging, these systems gradually deteriorate in time and approach the end of their lifespans. As a result, they require periodic maintenance and repair in order to function and be reliable throughout their lifetimes. Bridge infrastructure is an essential part of the transportation infrastructure. Bridge management systems (BMSs), used to monitor the condition and safety of the bridges in a bridge infrastructure, have evolved considerably in the past decades. The aim of BMSs is to use the resources in an optimal manner keeping the bridges out of risk of failure. The BMSs use the lifetime performance curves to predict the future condition of the bridge elements or bridges. The most widely implemented condition-based performance prediction and maintenance optimization model is the Markov Decision Process-based models (MDP). The importance of the Markov Decision Process-based model is that it defines the time-variant deterioration using the Markov Transition Probability Matrix and performs the lifetime cost optimization by finding the optimum maintenance policy. In this study, the Markov decision process-based model is examined and a computer program to find the optimal policy with discounted life-cycle cost is developed. The other performance prediction model investigated in this study is a probabilistic Bi-linear model which takes into account the uncertainties for the deterioration process and the application of maintenance actions by the use of random variables. As part of the study, in order to further analyze and develop the Bi-linear model, a Latin Hypercube Sampling-based (LHS) simulation program is also developed and integrated into the main computational algorithm which can produce condition, safety, and life-cycle cost profiles for bridge members with and without maintenance actions. Furthermore, a polynomial-based condition prediction is also examined as an alternative performance prediction model. This model is obtained from condition rating data by applying regression analysis. Regression-based performance curves are regenerated using the Latin Hypercube sampling method. Finally, the results from the Markov chain-based performance prediction are compared with Simulation-based Bi-linear prediction and the derivation of the transition probability matrix from simulated regression based condition profile is introduced as a newly developed approach. It has been observed that the results obtained from the Markov chain-based average condition rating profiles match well with those obtained from Simulation-based mean condition rating profiles. The result suggests that the Simulation-based condition prediction model may be considered as a potential model in future BMSs.

Pagrindinis šio darbo tikslas yra sukurti daugiapakopių procesų būsenų modelį, kuriuo būtų galima modeliuoti įvairių galimų bet kokios sistemos trikdžių scenarijus ir atlikti demonstracinius skaičiavimus. Atsiradus sutrikimui ar pažeidimams sutrikdomas kitų sistemoje dalyvaujančių pakopų darbas ir turime tam tikras pasekmes, kurios iššaukia problemas, liečiančias aplinkui funkcionuojančius sektorius. Todėl yra labai svarbu nustatyti daugiapakopių procesų būsenų modelio galimų būsenų scenarijus, išanalizuoti jų tikėtinumą bei dažnumą ir įvertinti. Daugiausiai dėmesio skiriama perėjimo tikimybių iš vienos pakopos būsenų į kitos pakopos būsenas matricų modeliavimui ir skaičiavimo algoritmo kūrimui. Tada atliekame stebėjimą kaip elgiasi trikdžių pasirodymo tikimybės per 100 perėjimų. Tam naudojami Markovo grandinės bei procesai ir tikimybiniai skirstiniai. / The main purpose of this research is to develop multi-stage process states model that could simulate a possible range of any system failures and demonstrational calculations. In the event of disruption or irregularities affects the other systems involved in stage work and we have certain consequences, which triggered concerns about the functioning around the sector. It is very important to establish a multi-stage process states model, the possible states of scenarios, analyze their probability and the frequency and to assess it. Focuses on the transition probabilities between states in the next tier level status matrix modeling and computing algorithm. Then perform the behavior tracking script and the likelihood of interference, the likelihood of the appearance of over 100 transitions. For this purpose, Markov chains and processes, and probabilistic distributions are used.