Methodical maintenance and renewal of infrastructure systems is critical due to the rapid deterioration of infrastructure assets under increasing loads and environmental effects and the scarcity of resources allocated for their preservation. A crucial step in pavement management is the formation and prioritization of maintenance and rehabilitation (M&R) projects that compete for limited funding for inclusion in the agency’s multiyear pavement management plan (PMPs). In general, many highway agencies perform this task subjectively, and thus a more rational and objective approach is desired to produce sound and justifiable PMPs. Specifically, such methodology should take into account the multiple factors that are considered by engineers in prioritizing M&R projects. This research addresses this need by developing a methodology for use by the Texas Department of Transportation (TxDOT) in preparing their four-year PMPs. Several key decision factors were considered and TxDOT decision makers were surveyed to weigh these factors as to their influence on prioritizing M&R projects. These were then used to develop a priority score for each candidate M&R project. Since TxDOT collects and stores data for individual 0.5-mile pavement sections, these sections must be grouped in a logical scheme to form realistic candidate M&R projects. The incremental benefit-cost analysis was performed on the candidate M&R projects to identify a set of M&R projects that maximizes network’s priority score under budgetary constraint. Future pavement condition was projected using performance prediction models and the process is repeated throughout the planning horizon to produce a multi-year pavement management plan. Data from Bryan district, which consists of 7,075 lane-miles of roadway, were used to develop and validate the PMP methodology. Comparison with the actual PMP (produced by TxDOT) shows some disagreements with the PMP generated by the methodology though the latter was shown to produce more cost-effective and defendable pavement management plans. Since the methodology is founded on TxDOT engineers’ decision criteria and preferences, they can be assured that the PMPs produced by this methodology are in line with their goals and priorities.

The transportation infrastructure systems in the United States were built between the 50's and 80's, with 20 years design life. As most of them already exceeded their original life expectancy, state transportation agencies (STAs) are now under increased needs to rebuild deteriorated transportation networks. For major highway maintenance projects, a federal rule enforces to perform a life-cycle cost analysis (LCCA). The lack of analytical methods for LCCA creates many challenges of STAs to comply with the rule. To address these critical issues, this study aims at developing a new methodology for quantifying the future maintenance cost to assist STAs in performing a LCCA. The major objectives of this research are twofold: 1) identify the critical factors that affect pavement performances; 2) develop a stochastic model that predicts future maintenance costs of flexible-type pavement in Texas. The study data were gathered through the Pavement Management Information System (PMIS) containing more than 190,000 highway sections in Texas. These data were then grouped by critical performance-driven factor which was identified by K-means cluster analysis. Many factors were evaluated to identify the most critical factors that affect pavement maintenance need. With these data, a series of regression analyses were carried out to develop predictive models. Lastly, a validation study with PRESS statistics was conducted to evaluate reliability of the model. The research results reveal that three factors, annual average temperature, annual precipitation, and pavement age, were the most critical factors under very low traffic volume conditions. This research effort was the first of its kind undertaken in this subject. The maintenance cost lookup tables and stochastic model will assist STAs in carrying out a LCCA, with the reliable estimation of maintenance costs. This research also provides the research community with the first view and systematic estimation method that STAs can use to determine long-term maintenance costs in estimating life-cycle costs. It will reduce the agency's expenses in the time and effort required for conducting a LCCA. Estimating long-term maintenance cost is a core component of the LCCA. Therefore, methods developed from this project have the great potential to improve the accuracy of LCCA.

The Texas Department of Transportation (TxDOT) uses the Texas Condition Assessment Program (TxCAP) to measure and compare the overall road maintenance conditions among its 25 districts. TxCAP combines data from three existing subsystems: the Pavement Management Information System (PMIS), which scores the condition of pavement; the Texas Maintenance Assessment Program (TxMAP), which evaluates roadside conditions; and the Texas Traffic Assessment Program (TxTAP), which evaluates the condition of signs, work zones, railroad crossings, and other traffic elements to get an overall picture of the condition of state roads. As a result, TxCAP provides a more comprehensive assessment of the interstate and non-interstate highways. However, the scores for each of the subsystems are based on data of different sample sizes, accuracy, and levels of variations, making it difficult to decide if the difference between two TxCAP score is a true difference or measurement error. Therefore, whether the use of TxCAP is an effective and consistent means to measure the TxDOT roadway maintenance conditions raises concerns and needs to be evaluated. In order to achieve this objective, statistical analyses of the system were conducted in two ways: 1) to determine whether sufficient samples are collected for each of the subsystems, and 2) to determine if the scores are statistically different from each other. A case study was conducted with a dataset covering the whole state from 2008 to 2010. The case study results show that the difference in scores between two districts are statistically significant for some of the districts and insignificant for some other districts. It is therefore recommended that TxDOT either compare the 25 districts by groups/tiers or increase the sample size of the data being collected to compare the districts as individual ones. / text

The performance of different pavement maintenance treatments were evaluated by investigating practical projects collected from Tennessee Pavement Management System (PMS) and Long Term Pavement Performance (LTPP) database. The influence of factors on the effectiveness, cost-effectiveness and cracking initiation of different treatment were evaluated by “Optime”, multiple linear regression and parametric survival analysis. Pavement roughness, pavement serviceability index (PSI) and the initiation time of cracking were used as pavement performance indicators. Investigation on the pavement maintenance projects in Tennessee by Optime and multiple linear regression analysis indicated that HMA overlay had the highest effectiveness, followed by mill & fill and micro surfacing. Due to the relatively low cost, micro surfacing was the most cost-effective treatment, followed by HMA overlay and mill & fill. The effectiveness and cost-effectiveness decreased with the increase of traffic level and pre-treatment pavement condition. Investigation on the LTPP resurfacing treatments indicated that thick overlay and milling reduced the roughness after rehabilitation. Thin overlay, high traffic level and poor pre-rehabilitation pavement condition increased the deterioration rate of new overlay. Using reclaimed asphalt material did not influence the treatment performance but was cost-effective in reducing the roughness of new overlay. For a certain deterioration rate, there was an optimized pre-rehabilitation roughness value or time for applying maintenance treatment. Survival analysis on the crack initiation of asphalt overlay indicated that high traffic level accelerated the initiation of cracking. Thick overlay delayed the initiation of cracking except for the non-wheel path longitudinal crack. Mill retarded the occurrence of the non-fatigue cracks, whereas severe freeze thaw condition accelerated the occurrence of the two types of cracking. Using 30% RAP accelerated the initiation of longitudinal fatigue crack on wheel path but did not cause serious fatigue problem. The performance curves of HMA resurfacing treatments used in Tennessee were calibrated by investigating the influence of different factors on the slopes and intercepts of post-treatment performance curves. The analysis indicated that pavement with high pre-treatment PSI, thick overlay and deep milling had low deterioration rate, whereas pavement with higher traffic level deteriorated faster.

The objectives of this research were to 1) compile winter maintenance data for the Utah Department of Transportation (UDOT) to directly compare concrete and asphalt pavements with regards to deicer usage and 2) determine if there is a statistical difference in deicer usage on concrete and asphalt pavements. To this end, three data sources were consulted for this research: Material Maintenance Quality Assurance (MMQA) database, UDOT road database, and Google Maps. The final compiled data set prepared for analysis in this research contained deicer quantities by deicer type, pavement surface areas by pavement material type, traffic, longitude, latitude, and elevation data. The deicer data evaluated in this analysis represented the total quantities of each deicer distributed during the 8-year period during which the MMQA database was used by UDOT.Several multiple linear regression analyses were performed to determine if concrete or asphalt pavements required different amounts of deicers, including salt, Redmond salt, brine, wetted salt, magnesium chloride, sand, pre-mix, and wetted pre-mix, during the winter seasons evaluated in this research. Because plow routes were not equal in total pavement area, a variable called “concrete proportion” was created. Similarly, traffic and deicer quantities were divided by total pavement area in lane miles to account for the variation in maintenance station sizes and to allow for direct comparison of the various maintenance stations. After the values of the independent variables were finalized, full and reduced models were created for the total amount of all deicers per lane mile and the amounts of each of the eight individual deicers per lane mile based on the statistical significance of the respective independent variables. A total of 18 regression models were completed for this research.From the results of the statistical analyses, concrete proportion was statistically significant in models for three of the dependent variables, including brine, wetted salt, and wetted pre-mix. However, neither the full nor the reduced regression model prepared for the sum of all deicers had concrete proportion as one of the significant variables. The absence of concrete proportion as an independent variable in these models shows that, on average, after correcting for differences in traffic volume and pavement area, deicer usage in Utah is not affected by pavement type. Therefore, except in areas where applications of brine, wetted salt, and wetted pre-mix are common, winter maintenance costs should not be a factor in the determination of pavement type.

No description available.

Infrastructure systems are critical to sustaining and improving economical growth. Poor condition of infrastructure systems results in lost productivity and reduces the quality of life. Today's global economy forces governments to sustain and renew infrastructure systems already in place in order to remain competitive and productive (GAO, 2008). Therefore, civil engineers and policymakers have been quite interested in the overall quality of the highways and bridges throughout the US (Miller, 2007). Transportation networks are essential parts of the Nation's infrastructure systems. Deterioration due to age and use is the main threat to the level of service observed in surface transportation networks. Thus, highway agencies throughout the United States strive to maintain, repair and renew transportation systems already in place (Miller, 2007). A recent disaster, the collapse of the Minneapolis I-35 W Bridge, once again revealed the importance of infrastructure preservation programs and resulted in debates as to how state departments of transportation (DOTs) should and can preserve the existing infrastructure systems. Therefore, it is essential to establish effective maintenance programs to preserve aging infrastructure systems. The major challenge facing the state highway maintenance managers today is to preserve the road networks at an acceptable level of serviceability subject to the stringent yearly maintenance and rehabilitation (M&R) budgets. Maintenance managers must allocate such limited budgets among competing alternatives, which makes the situation even more challenging. Insufficient use of available smart decision-making tools impedes eliciting effective and efficient maintenance programs. Hence, this thesis presents the development and implementation of a network-level pavement maintenance optimization model which can be used by maintenance managers as a decision-making tool to address the maintenance budget allocation issue. The network-level optimization model is established with the application of the Linear Programming algorithm and is subject to budget constraints and the agencies' pavement performance goals in terms of total lane-miles in each pavement condition state. This tool is developed with Microsoft Office Excel. The tool can compute the optimal amount of investment for each pavement treatment type in a given funding period. Thus, the model enables maintenance managers in highway agencies to develop alternative network-level pavement maintenance strategies through an automated and optimized process rather than using what-if analysis. / Master of Science

Sustainability is increasingly becoming a significant part of strategic asset management worldwide. Road agencies are providing guidelines to assess the relative sustainability of road projects. Unfortunately, environmental features of a road project are still considered as stand-alone evaluations, an added value. Very little has been done to integrate environmental impacts as a part of pavement management systems and other decision support tools to choose between different strategies. In this way, being awarded with a "green" certificate for a specific road project could result in the belief that recognition would correspond to the optimal strategy. Furthermore, a road project awarded with a "green" rating during the construction phase does not mean that the project results "green" if a life cycle approach is considered. Indeed, the most environmental friendly strategies may not be the ones with the highest performance. Using "greener" materials or performing recycle-related practices may lead to a lower performance over the life cycle and therefore produce an increase in maintenance needed, which could in turn result into more congestion due to work zones and higher total emissions. Therefore, construction and maintenance strategies should be analyzed according to three main parameters: cost, performance or effectiveness, and environmental impacts. The cost analysis part takes into account outflows over the service life of the pavement according to the well-known Life Cycle Cost Analysis methodology. The cheapest maintenance technique over the analysis period was expounded and sensitivity analyses to involved factors were conducted. Performance assessment was developed according to experimental on site data gathered and analyzed over several years to develop deterioration pavement models. Effectiveness of maintenance treatments is further provided and compared to the volume of traffic. In addition, environmental impacts related to maintenance and rehabilitation strategies were analyzed. Emissions were computed over the life cycle of the pavement from the manufacture of raw materials for the initial construction, placement, and maintenance phase. Finally, an optimization procedure was developed for including environmental impacts into a Pavement Management System. A methodology to set a multi-attribute approach system, computing costs, performance, and eco-efficiency over the life cycle of the pavement, is therefore proposed. / Ph. D.

Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第16818号 / 工博第3539号 / 新制||工||1535(附属図書館) / 29493 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 教授 大津 宏康, 教授 河野 広隆 / 学位規則第4条第1項該当

Τα τελευταία χρόνια στις περισσότερες χώρες έχει ολοκληρωθεί η κατασκευή οδικών δικτύων και το ενδιαφέρον των φορέων οδοποιίας έχει στραφεί στη διαχείριση των υφιστάμενων οδικών κατασκευών. Το κυριότερο τμήμα της Διαχείρισης Οδικών Δικτύων καταλαμβάνει η Διαχείριση Οδοστρωμάτων. Τα Συστήματα Διαχείρισης Οδοστρωμάτων έχουν ως στόχο την οικονομική διαχείριση των οδοστρωμάτων και χρησιμοποιούν τεχνητή νοημοσύνη για να καταλήξουν στη βέλτιστη και οικονομικά αποδοτικότερη κατανομή των διαθέσιμων πόρων. Το ευφυές σύστημα που διαθέτουν καταφέρνει να εντοπίζει τη βέλτιστη λύση που ελαχιστοποιεί το κόστος συντήρησης αλλά δεν λαμβάνουν υπόψη τους το αντίκτυπο της επιδείνωσης της κατάστασης του οδοστρώματος στο χρήστη και στο περιβάλλον. Στην παρούσα εργασία χρησιμοποιείται ένας γενετικός αλγόριθμος και αναζητείται η βέλτιστη λύση που ελαχιστοποιεί το γενικευμένο κόστος, το οποίο περιλαμβάνει το κόστος συντήρησης, το κόστος του χρήστη εξαιτίας της κατάστασης του οδοστρώματος και το περιβαλλοντικό κόστος. Τα δεδομένα του προβλήματος αφορούν την κατάσταση των τμημάτων που πρόκειται να συντηρηθούν, το είδος της οδού στο οποίο ανήκουν τα τμήματα οδοστρώματος, τα στοιχεία φθορών κάθε τμήματος, τα διαθέσιμα είδη συντήρησης, το ύψος της χρηματοδότησης και τα κυκλοφοριακά χαρακτηριστικά της περιοχής στην οποία βρίσκονται τα υπό συντήρηση τμήματα. Ο αλγόριθμος κατασκευάζει γονίδια επιλέγοντας είδος συντήρησης για κάθε τμήμα και για κάθε χρόνο συμπεριλαμβανομένης και της επιλογής να μην γίνει καμία συντήρηση σε κάποιο χρόνο. Τα γονίδια ελέγχονται με βάση περιορισμούς που έχουν τεθεί από τα μοντέλα φθορών κάθε τμήματος και επιλέγονται να μεταφερθούν στην επόμενη γενιά αυτά που συνδυάζουν το ελάχιστο κόστος και το μέγιστο επίπεδο λειτουργικότητας στο οδόστρωμα. Η διαφορά του μοντέλου σε σχέση με τα κοινά συστήματα διαχείρισης οδοστρωμάτων έγκειται περισσότερο στις υπολογιστικές απαιτήσεις του συστήματος καθώς η εφαρμογή γενετικού αλγορίθμου οδηγεί γρηγορότερα σε λύση από ότι οι κλασικές μέθοδοι βελτιστοποίησης όπως π.χ. ο γραμμικός προγραμματισμός. Η καταλληλότητα και η ευκολία προσαρμογής των γενετικών αλγορίθμων σε προβλήματα διαχείρισης οδοστρωμάτων επαληθεύεται στην παρούσα εργασία. Το σύστημα καταφέρνει να εντοπίζει το βέλτιστο χρόνο με την οικονομικότερη συντήρηση του κάθε τμήματος του οδικού δικτύου και την πιο φιλική λύση για το χρήστη της οδού και το περιβάλλον. / In recent years the focus of the transportation authorities, researchers and practitioners is being shifted from the construction of new roads to the management of existing road structures and especially to road pavements. Pavement Management Systems are widely used and are continuously being improved because they can lead to considerable fund savings and/or to higher levels of service of road pavements. In this work, a model for pavement maintenance and rehabilitation planning and optimal resource allocation is presented. The objective function aims at minimizing a generalized cost parameter which includes a number of monetary cost components and no monetary impacts. In particular, the objective function consists of the following components: (1) agency cost (the cost of applying the selected maintenance and rehabilitation strategy), (2) user costs (they include vehicle operating cost for fuel consumption, vehicle maintenance and depreciation, traffic delay cost, accident cost, discomfort cost, and delay cost due to maintenance works and (3) environmental impact costs due to traffic pollution and noise. The above cost components are considered with regard to the existing pavement condition levels which are represented by the PSI index. Pavement condition deterioration is assessed through deterministic models that have been developed earlier by our team based on expert opinions and fuzzy systems considering pavement related and traffic parameters, i.e., pavement age, pavement strength, pavement construction quality and traffic loads. The maintenance and rehabilitation treatments are considered with regard to their cost and effectiveness characteristics. Besides the pavement condition deterioration functions, other constraints of the model include budgetary availability (total and individually for different highway groups), threshold values for the minimum accepted pavement condition levels (by highway class), desirable pavement condition levels (by highway class), maintenance and rehabilitation treatment applicability and effectiveness, etc. Due to the size and complexity of the problem (non linear functions), a genetic algorithm has been used as an optimization tool. The algorithm forms solutions by considering applicable maintenance treatments at each pavement section and year within the analysis period. Each solution is checked against all constraints to ensure the feasibility of the solution. No feasible solutions are discarded and new solutions are generated until the required offspring solutions are obtained. The optimization runs over several road sections with different traffic and pavement condition characteristics and within a time span of 10 years. The budgetary or the minimum accepted pavement condition constraints can be altered in order to get a Pareto-front set of optimal solutions for a particular application. Preliminary evaluation indicates that the model provides reasonable results in terms of the appropriate selection of maintenance and rehabilitation treatments and the time of application.