Journal of Infrastructure Systems

A decision framework is introduced to assist municipal engineers and planners to optimize decisions regarding the renewal of large infrastructure assets such as water transmission pipes, trunk sewers, or other assets with high costs of failure, inspection, and condition assessment. The proposed decision framework identifies a need for immediate intervention or, alternatively, enables optimization of the scheduling of the next inspection and condition assessment. The deterioration of the asset is modeled as a semi-Markov process and is thereby discretized into condition states. The waiting times in each state are assumed to be random variables with “known” probability distributions. If pertinent data are scarce (as is typical in most municipalities) these probability distributions can be initially derived based on expert opinion. These distributions will then be continually updated as observed deterioration data are collected over time. Monte Carlo simulation is used to calculate the distributions of the cumulative waiting times. Conditional survival probabilities are used to compile age-dependent transition probability matrices in the various states. The expected discounted total cost associated with an asset (including cost of intervention, inspection, and failure) is computed as a function of time. The time to schedule the next inspection/condition assessment is when the total expected discounted cost is minimum. Immediate intervention should be planned if the time of minimum cost is less than a threshold period (2 to 3 years) away. A computer program is prepared for demonstration and proof of concept. The decision framework lends itself to a computer application fairly easily. Although usable in its current form, this paper identifies some issues that require as yet unavailable data as well as more research in order to develop the framework into a comprehensive application tool.

The principal objective of this paper is to set up an evaluation multiple layer fuzzy method model for evaluating the damage stage of existing reinforced concrete bridges. After establishing the detailed evaluation supportability and antiseismic ability of existing bridges items, the fuzzy mathematical theory is adopted to evaluate the damage state of any member of an existing bridge. The damage state of any member of an existing bridge is systematically and completely composed as an evaluation model of multiple layer fuzzy mathematics. The evaluated results may be used for the safety index and reference index for repair or reinforcement in existing bridges. In addition, the evaluated results may also be used as a design reference for service life in future bridge building. The evaluated model may be divided into the degrees of grades I, II, III, IV, and V, which are described as nondamage, light damage, moderate damage, severe damage, and unfit for service, respectively. Using the proposed model, the Huey-tong bridge, Jzyh-chyang bridge, Ay-gwo west road viaduct, and old Hwan-nan viaduct in Taipei were chosen for evaluation. The evaluated results of the present investigation indicate that the order of repair and reinforcement is the Ay-gwo west road viaduct, Huey-tong bridge, old Hwan-nan viaduct, and Jzyh-chyang bridge. Thus, the multiple layer fuzzy method appears the advantage for evaluating the damage of existing reinforced concrete bridges.

Use of various deterioration models in the area of infrastructure management has provided decision makers with a vehicle for predicting future deterioration. This paper presents a methodology for predicting the likelihood that a particular infrastructure system is in a deficient state, using logistic regression models, a special case of linear regression. What distinguishes these two models is that the outcome variable in the logistic regression model is binary or dichotomous and assumes a Bernoulli distribution. The methodology is illustrated in a case study involving the evaluation of the local sewer system of Edmonton, Alta. Canada. Variables of age, diameter, material, waste type, and average depth of cover are modeled, using historical data, as factors contributing to deterioration of the sewer network. The outcome of this model does not produce a prediction of condition rating but rather uses historical inspection records to provide decision makers with a means of evaluating sewer sections for the planning of future scheduled inspection, based on the deficiency probability.

Certain highway agencies such as the Florida Department of Transportation use the crack index (CI) to enumerate pavement cracking and determine the rehabilitation priorities. Thus, accurate forecasting of CI is essential for pavement rehabilitation budgeting. Currently, mechanistic-empirical and purely empirical models are popular tools for forecasting pavement cracking. However, with a large data dimension, it is difficult to select appropriate mathematical function forms for the above models. This paper summarizes the results obtained from a case study in which single-year and multiyear back-propagation neural network (BPNN) models were developed to forecast accurately the short-term time variation of CIs of Florida's highway network. The BPNN models exhibited a remarkable ability of learning the historical crack progression trend from the CI database and accurately forecasting future CI values. Then, the BPNN model was validated by comparing the forecasted CIs with measured CI data for the year 1998. Finally, the BPNN model results were compared to those of a commonly used autoregressive model and the BPNN model was seen to be certainly more accurate than the autoregressive model. Hence the BPNN models can be expected to make a significant impact on the efficiency of rehabilitation budget planning in particular and pavement management systems in general.