Journal of Infrastructure Systems

The intelligent renewal of our aging civil infrastructure systems includes efficient use of smart materials and structures. In this viewpoint some examples of projects funded by the National Science Foundation on smart materials and structural systems are presented. Basic research in designed materials, smart materials and the associated structures, and developing research results, has great potential for enhancing the functionality, serviceability and enhanced life span of our civil infrastructure systems and as a result, will contribute to the improvement of productivity and quality of life.

A general four-tier reassessment and requalification engineering system is developed. The first-tier approach is the simplest. It is based on a qualitative scoring process. The fourth-tier approach is the most complex. It is based on a detailed study of the nonlinear dynamic performance characteristics of the structure when it is subjected to extreme conditions. The primary objective of this system is to qualify structures at the simplest possible level. Requalification or fitness-for-purpose guidelines have been developed based on economic, historic, and standard-of-practice evaluation procedures. Probabilistic and decision analysis methods underlie both the reassessment and requalification approaches. This reassessment and requalification system has potential applications to a wide variety of infrastructures. Application to offshore platforms is illustrated.

This paper presents a methodology to analyze the reliability of a regional water distribution network. The proposed reliability analysis method focuses on the impact of link failures on source-demand connectivity, which is used as a measure of the mechanical reliability of the network. The mechanical reliability index is computed using the minimum cut-set method. The identification of minimum cut-sets consists of four stages: (1) For source-demand pairs; (2) for individual demand nodes; (3) for a group of demand nodes; and (4) for all demand nodes in the system. By using the multiple-stage approach, the total number of simulations required in the analysis is greatly reduced. Computational efficiency of the minimum cut-set method is further enhanced by using a highly efficient embedded generalized network algorithm to solve the optimization model used in the simulations. The results from the reliability analysis can be used to locate crucial pipelines in the network whose failure will severely impair the source-demand connectivity. The developed methodology is applied to a large-scale regional water distribution network associated with the Metropolitan Water District of Southern California.

Stochastic simulation is applied in a reliability analysis of a regional water distribution network. To complement the connectivity-based reliability analysis presented in the companion paper, the proposed stochastic simulation is aimed at evaluating the impacts of component failures on meeting demand at a certain quantity level. Simulation is conducted in a manner such that a sampling experiment of the system performance is repeated a sufficient number of times and the results are analyzed to obtain the desired reliability index. Given the mean time to failure and repair time of links, a large number of synthetic system conditions are generated and operations of system under the different conditions are simulated. Stochastic simulation of the system is conducted with a highly efficient embedded generalized network flow model (EMNET) to determine the optimal system operations for each scenario. The proposed reliability analysis method is applied to the water distribution network of the Metropolitan Water District of Southern California (MWDSC).

Rail fatigue failures are of great concern to railroad companies because they may cause derailments. Therefore, the railroad companies' objective is to predict the locations and times of potential failures and perform preventive maintenance. To predict when and where rail fatigue failures will occur, it is necessary to establish the relationship between fatigue failures and factors affecting fatigue. This paper specifies a statistical rail fatigue model in four stages: first, the model describes the relationship between probability of failure and usage; second, the model is extended to cases of multiple types of failures; third, the model is derived for discrete usage categories or time periods with a continuous spatial representation of rail units; and finally, explanatory variables capturing geometry, maintenance, and material properties are included to explain differences in fatigue behavior among heterogeneous rail units. The model is estimated using available rail fatigue data. The modeling framework can be applied to other multitype failure behavior and to reliability problems such as vehicle breakdown.

This paper presents an analytical framework and numerical policy simulations that assess the relative role of federal and state governments in pollution control management in decentralized economies. The framework is designed as a multilevel Stackelberg game. It reproduces the hierarchical structure of pollution control policies in Brazil where the federal environmental protection agency (FEPA) relies on the State Environmental Protection Agencies (SEPAs) to ensure that federally defined minimum ambient standards are met locally. It is the replication of this incentive structure that makes this study Brazil-specific. This paper makes the case for a federal role in monitoring SEPAs pollution control performance as states face a trade-off between the desire to raise revenue from taxes on local output and the need to limit pollution due to production. It also shows to what extent fines and pollution taxes, assigned respectively to federal and state governments, can improve compliance of firms and SEPAs, and environmental quality without damaging or improving states' revenue. A model is applied to the food industry (as a representative of a clean industry) and the printing and publishing industry (as a representative of a dirty industry). The results of the model, however, have broader implications for pollution control of water systems in general.

Ground-penetrating radar (GPR) technology has been applied to the evaluation of pavements, bridge decks, abutments, piers, and other constructed facilities to assess as-built conditions and to evaluate damage and deterioration that develops over time. Two basic types of GPR equipment systems are used depending on the type of antenna employed: either ground-coupled antennas or air-coupled horn antennas. Ground-coupled equipment is most suitable for deeper penetrations and for object detection, and where survey speed is not critical. Horn antenna equipment is most suited for driving speed measurements, and where quantitative high resolution results are required. To evaluate the data, two types of data processing are employed: (1) qualitative assessments and manual calculations from graphically displayed data, which is most suited to site-specific evaluations; and (2) automated processing of the raw radar waveforms, which is most suited to production surveys such as pavements and bridge decks. In some applications, such as the measurement of pavement thickness and depth of reinforcement, research studies have verified the GPR findings. In other applications, such as the detection of voids under concrete, the technique can only be reliably used on a site-specific basis, and further development and verification is required before GPR can be considered more generally applicable. New technologies are currently under development that offer the potential to enhance the range of applicability of the GPR technique.