Journal of Infrastructure Systems

This paper presents a novel methodology for the nondestructive evaluation of brick walls using fuzzy logic and summarizes the results of the nondestructive evaluation of the relative condition of a sidewall of a brick masonry channel using the multichannel analysis of surface waves (MASW) test. The new methodology addresses the critical problem of considering the inherent variability of field data in the analysis of seismic tests. Wave propagation measurements were taken on the sidewall at 161 locations; the total length of wall tested was $80\mathrm{m}$ . Each MASW test consisted of the simultaneous measurement of the impact response of the wall with an array of 15 transducers (accelerometers); each test assessed the relative condition of a wall section $7.5\mathrm{m}$ long. For one of the sections, measurements were taken before and after a uniform grouting. The relative condition of the wall is evaluated by considering five wave propagation characteristics: group velocity, minimum and maximum phase velocities, predominant frequency, and attenuation coefficient. A fuzzy logic model is developed based on expert knowledge to identify the weaker sections of the sidewalls. The output variable of the model is a condition index that classifies the relative condition of sections in five linguistic categories from “very good” to “damaged” conditions. The effect of each wave propagation characteristics on the condition index is evaluated through a sensitivity analysis.

Deterioration modeling plays a central role in performance prediction, a key component in transportation infrastructure system management. The deterioration of a facility can be expressed in terms of numerous indicators. For instance, in the case of highway pavements, these indicators range from panel ratings (such as pavement serviceability rating) to distress variables (such as percentage of surface cracking and rut depth). Different indicators capture different deterioration mechanisms; however, they may be correlated to each other. Numerous studies have been conducted involving modeling the deterioration of transportation facilities. Empirical regression has proved to be an effective way to characterize the relationship between the explanatory variables and dependent variable (performance indicator). The majority of these models were established with the focus on each individual performance indicator separately and independently. However, one type of indicator does not sufficiently capture the performance of a facility. For instance, while a softer binder may improve fatigue cracking performance, it could possibly be detrimental to rutting performance. As a result, the facility deterioration mechanism may not be fully characterized by single models. Since decision making in infrastructure management usually relies on a series of different performance indicators, the systematic considerations of these indicators is imperative. In this study, a flexible and statistically sound modeling approach is presented to jointly account for the performance of a transportation facility based on two indicators. A system of nonlinear structured econometric models is established to capture different deterioration processes in terms of these indicators. Due to the fact that the selected indicators represent the performance of a single section from different aspects, the correlation among the models in the system is particularly addressed. A case study is presented to demonstrate the feasibility of the proposed methodology with focus on in-service highway pavements. Two major indicators, international roughness index and rut depth, widely used in characterizing flexible pavement performance, are adopted and analyzed in the model system. The results demonstrate that: (1) different indicators are associated with different deterioration mechanisms reflected through their respective parameters; (2) the correlation of different deterioration mechanisms and relation of parameters across the models can be captured through relevant statistics; and (3) model estimation is efficient when accounting for the correlation among the models.

We conduct an empirical comparison of nine representative statistical pavement performance models, which have been estimated using serviceability data from the AASHO Road Test. The purpose of the study is to understand the effect of different statistical assumptions and estimation techniques on the models’ predictive capabilities. The study consists of using the models to predict the serviceability of a common subset of pavements from the test over a two-year forecast horizon. Comparison of the models is carried out using both aggregate- and disaggregate-level measures. The main insight that stems from our study is that models that account for heterogeneity lead to improved predictive capabilities. This is particularly important because the data were obtained from a controlled experiment. The study further shows that disaggregate measures are more useful in testing performance models because they are better indicators of the ability to capture the variability in data. Throughout the paper, we also discuss other model characteristics and issues that can be useful in the future development and evaluation of performance models.

This paper focuses upon the optimal repair and inspection policies of infrastructure under uncertainty in the facility deterioration process. It is supposed that the condition of the facility, which is defined in continuous state, can only be observed by inspection. The optimal impulse control problem is formulated to find out the optimal timing for inspection and repairing so as to minimize the expected life-cycle costs, including the repairing cost, user’s cost, and inspection cost. The state-dependent rules are presented to determine the optimal timing for inspection and repairing based upon the observation of condition state by solving the second-order Fredholm integral equation. The paper concludes with numerical examples, for illustration.

In this paper, a pavement management accounting system (PMAS) is presented as a method for controlling the life cycle costs of road pavement. The aim of the PMAS is to provide decision makers with planning information regarding repair strategies and accounting information on asset values, with the aim of achieving efficient asset management of road pavement. This paper represents implementation of an integrated pavement system with an accounting system. This integration makes it possible to provide justification for a specific budget request on the basis of standard cost accounting information. Moreover, it links pavement deterioration explicitly to asset depreciation, which makes it possible to combine engineering and accounting information within the same analysis. By comparing the pros and cons of the three alternative methods of accounting, the authors conclude that deferred maintenance accounting is most likely to result in the efficient asset management of road pavement. The PMAS for asset management is constructed and implemented by applying the maintenance accounting scheme. The validity of the PMAS presented in this paper is investigated through case studies conducted for Mie Prefecture.