Journal of Infrastructure Systems

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

This paper shows that the choice of the type of probability distribution is crucial in Real Options Analysis, because it could lead to different outcomes. This is illustrated by using the beta distribution and its special cases, such as the Program Evaluation and Review Technique (PERT) and uniform distribution to model parking garage demand uncertainty. These distributions are commonly used when there are no data available about the stochastic variable, i.e., demand uncertainty. Beta distributions are more flexible than the PERT and uniform distribution. One of the major challenges is the practicality of the beta distribution. A good solution to this challenge is provided by the PERT distribution, because of its ease-of-use and greater flexibility than the uniform distribution. In this research, a parking garage case example is used as the base case for modeling of the parking garage demand and is refined to allow for more generic, flexible, and practical applications of the model. The impact of different probability distributions is studied on the basis of an expansion option.

In the management of road networks, it is often desired to know the condition of individual road sections, which is approximated using the values of condition indicators. The values of these indicators can be used, for example, to determine whether an intervention should be executed on the road sections in the upcoming year, or to predict the future condition of the road sections. Unfortunately, a common problem when working with these data is that there are numerous road sections where no information is available. This can happen either due to errors made during the inspection campaigns themselves or due to using multiple independent sets of geographical information system (GIS) indexed data, when the sets are recorded as noncoincidental GIS shapes. It is of interest to the road manager to estimate the values of the missing condition indicators as best as possible. In this paper, an investigation of the ability to estimate values of road section indicators based on their spatial correlation is presented. The investigation is done by estimating the values of condition indicators for surface defects, and longitudinal and transversal unevenness exploiting the spatial correlation between them, on the Swiss national highway network. It is shown that the values of road section indicators can be estimated based on their spatial correlation with reasonably high levels of accuracy. The variation of the predictive ability per condition indicator is shown.

Industrialization has degraded water resources over the last century due to increasing stormwater runoff. Increased impervious area utilization has substantially reduced infiltration into the ground, raised flood risk, and transferred contaminant materials into water bodies. Sustainable stormwater management is needed in order to manage runoff in urban areas and prevent pollution from water resources. Low-impact development (LID) practices, such as permeable pavement, manage a large volume of surface runoff during rain events and prevent combined sewer systems from overflowing. Predicting the captured runoff volume from watershed area by permeable pavements provides useful resources to achieve more efficient designs. Artificial neural network (ANN) models have been developed to predict the captured runoff with higher accuracy. The ANN models relate rainfall parameters and site characteristics to the stored runoff volume. A comprehensive database is obtained from the recorded data of the monitored two permeable pavements over a 2-year period. The performances of the ANN-based models are analyzed and the results demonstrate that the accuracy of the proposed models is satisfactory as compared to the measured values. Sensitivity analyses are conducted to calculate the relative importance of the studied parameters on the stored runoff. It was concluded that the ANN models are accurately predicting the stored runoff during different rain events and site characteristics. The ANN models consider the contributing parameters and provide precise volume estimation in comparison with the linear model. The results of the prediction models are useful to schedule the efficient maintenances and achieve better permeable pavement performance.

France comprises 10,000 km of river levees. Those levees show highly variable performances caused by the various types of design and construction, age, environment, loadings, and maintenance and repair works performed on them. Available information on levees also varies in quality and most often proves to be incomplete, uncertain, or imprecise. Based on this, a method for providing a quantitative evaluation of levee performance is suggested. Such a method, based on a multicriteria decision-making model and subjective probabilities, takes all available pieces of information into account—design, construction, visual inspections, and geophysical and geotechnical investigations—and reckons them with the level of uncertainty of the available data. This method provides decision-making support in identifying the least efficient sections and setting up investigation and maintenance programs.

The main goals of this study are to provide an improved interpretation of flexural vibration modes of delaminations in concrete bridge decks detected using impact-echo testing, and to develop a simple two-dimensional (2D) visualization scheme to show the areal extent of likely delaminated regions. To achieve those goals, the study consisted of three main tasks. The first was development of an analytical formulation that relates the fundamental frequency of the response to the geometry of delamination in a concrete plate. The formulation was developed from the thin plate vibration theory and numerical model simulations. The second task was the development of a simple model for predicting dynamic behavior (flexural vibration) of delamination in concrete plates. Finally, in the third task, the validity of the proposed model was verified through a comparison of the results from an experimental study on a near-real-scale concrete bridge deck and the results reported in prior studies. As demonstrated, the developed model enables better understanding and interpretation of the dynamic response of delamination in reinforced concrete structures.

Many bridge management systems (BMSs) use a Markov chain model to forecast the deterioration process. The Markov property may be considered to be restrictive when modeling the deterioration of transportation assets, primarily because of the memoryless property and assumption of exponential distribution for sojourn times in the condition states. This study addresses some of the limitations that arise from the use of purely Markov chain deterioration models for transportation infrastructure by introducing alternative approaches that are based on the semi-Markov process. Two semi-Markov approaches for modeling the deterioration of certain bridge elements are developed. These are compared against a previously developed semi-Markov approach, the traditional Markov chain deterioration approach, and the change in the average actual condition indices of bridge elements that deteriorated for 8 years after being constructed. The results obtained from this study indicated that semi-Markov models are feasible and more flexible than the traditional Markov chain models when forecasting the future deterioration of transportation infrastructure. The semi-Markov models that were developed produced condition indices that were closer to the values of the average actual condition indices of bridge elements when compared against the values based on the traditional Markov chain model.

Infrastructure has received much attention in recent years. Investment in infrastructure is particularly effective and has been recommended to institutional investors for investments such as pension funds because of the characteristics of infrastructure assets. However, robust analytical and empirical analyses in support of these investments are limited, mainly due to scant empirical data. In this work, by collecting relevant data sets on infrastructures, the authors address two objectives. First, the authors examine the significance of listed infrastructure sectors and subsectors by assessing the investment characteristics and performance of different infrastructure indexes in Europe. The aim here is to demonstrate how an effective and successful infrastructure portfolio should be constructed. The second objective of this research is to evaluate the strategy of infrastructure investors, in other words, to prove evidentially whether the investor should invest in a portfolio containing different infrastructure sectors or whether it is still possible to obtain diversification benefits by investing in only a single infrastructure sector.

Because of their potential danger to public health, economic loss, environmental damage, and energy waste, underground water pipelines leaks have received more attention globally. Researchers have proposed active leakage control approaches to localize, locate, and pinpoint leaks. Noise loggers have usually been used only for localizing leaks while other tools were used for locating and pinpointing. These approaches have resulted in additional cost and time. Thus, regression and artificial neural network (ANN) models were developed in this study to localize and locate leaks in water pipelines using noise loggers. Several lab experiments have been conducted to simulate actual leaks in a sample ductile iron pipeline distribution network with valves. The noise loggers were used to detect these leaks and record their noise readings. The recorded noise readings were then used as input data for the developed models. The ANN models outperformed regression models during testing. Moreover, ANN models were successfully validated using an actual case study.

This paper compares dependencies and interdependencies between power and telecommunication infrastructures during the 2011 $M_W$ 9.0 Tohoku earthquake in Japan with those in the 2010 $M_W$ 8.8 Maule offshore earthquake in Chile. This comparison is important to assess the extent to which particular failure modes and restoration processes are prevalent in two different cases, especially when quantification of infrastructure coupling is still in its infancy. These comparative observations are also relevant for the design and improvement of critical infrastructure and associated resilience. The analysis is conducted by comparing time series of power and telecommunication system damage, outages, and service restoration as field-collected in the regions affected by the respective earthquakes. The metrics used for the comparison, namely the dependence predominance and the restoration delay index, are based on cross-correlation functions and a measure of dependence predominance based on the peak cumulative cross-correlations and dominant restoration times between the two infrastructures. Results indicate that there is a strong coupling between the restoration of power and telecommunication infrastructure systems in both Japan and Chile events. For instance, the power system outage restoration process in Japan led the restoration of mobile communication networks more strongly than of that of landline telephony systems, confirming similar observations from the February 2010 earthquake in Chile, While in both Chile and Japan, the landline and mobile system restoration processes tended to occur together, likely due to infrastructure collocation in the switching centers and common transmission links. The trends from these two events provide evidence of the significant dependence of communication systems on power supply infrastructure, indicating the need for novel technological alternatives, such as microgrids, to improve communication networks’ performance during disasters and set the foundation for future disaster-resilient smart grid systems.

Construction site layout optimization (CSLO) involves many important issues vital for the success of construction projects. One of the most important is the hazardous-material transportation (HT) problem. This paper concurrently considers CSLO and HT, namely the CSLO-HT problem, and proposes a bilevel multiobjective decision-making model (BMDMM). In this model, the upper-level decision-maker is the project manager, who aims to minimize site layout costs and economic losses from potential HT accidents. The lower-level decision-maker is the carrier, to whom the HT work is subcontracted, and whose goal is to reduce transportation costs. To solve the proposed bi-level multiobjective model, a fuzzy random simulation–based bilevel multiobjective genetic algorithm (frs-BLMOGA) is proposed. The approach is then applied to a hydropower construction project to illustrate the performance of the proposed methodology. The results and further analyses of the methodology prove that both the project manager and the carrier can get benefits. Particularly, satisfactory CSLO-HT solutions are obtained.

Ecoroof systems are proliferating to meet the need for reducing peak storm discharge and improving habitat in urban environments; however, no coherent set of standards exists for the engineering of these systems. Necessary questions that remain regarding engineering performance range from hydraulic to structural to geotechnical in nature, as exemplified in a series of recent ecoroof failures. As a first step, this paper describes a field exploration and laboratory characterization program conducted to develop a necessary geotechnical engineering baseline for ecoroof soils currently used with sustainable green-roof technologies. A series of low-stress (i.e., 10–30 kPa), static drained, simple shear tests were conducted on both reconstituted and relatively undisturbed specimens derived from three target gradations. The volumetric response of the undisturbed specimens exhibited contraction, and developed significantly larger magnitudes of volumetric strain than that of the reconstituted specimens, which exhibited a range in shear strain-dependent volumetric responses. The mobilized friction angle of the ecoroof materials ranged from approximately 37 degrees to 65 degrees, and varied as a function of organic content, normal stress, gradation, and relative density. The results show that the organic content has a significant effect on the stress-strain and volumetric responses of ecoroof soil in the field, which indicates that ecoroof design codes and standards are needed.

The allocation of risk between public-sector and private-sector parties in a public–private partnership (PPP) contract is a critical element of success that should be based on an assessment of the party best able to manage it. Assessing the risk-management capability of a contracting party to reach an efficient risk-allocation decision is subjective and implicit because it requires value judgments and experiential knowledge of expert practitioners. This paper draws on a set of established risk-allocation criteria (RAC) and the fuzzy-set approach to examine the allocation of five key risk factors related to PPPs in water supply infrastructure projects. The fuzzy set is used to deal with ambiguity in the linguistic RAC to minimize the fuzziness and bias in qualitative expert knowledge that characterize real-life decision-making. To explore how the risks should be allocated, three rounds of a Delphi questionnaire survey was conducted to elicit knowledge of practitioners with hands-on experience related to PPP projects in the water sector by requesting them to evaluate the risk factors on each risk-allocation criterion. The RAC and experiential knowledge were modeled to reflect capabilities of the public–private parties and to suggest the risk-allocation decisions for the corresponding risks. The results show that the risks should be shared between the government and the private sector, rather than wholly transferring specific risks to a particular party. This paper shows that it is possible to effectively distribute risks between the public–private parties based on fuzzy-set theory, RAC, and qualitative expert knowledge. The method has a merit of enabling decision-makers to utilize qualitative linguistic terms to assess risk-management capabilities of public–private parties and of predicting risk-allocation strategies in a contract. Also, the paper hints that, in developing countries, governments may take some political actions to make a water-infrastructure project to be executed via the PPP mode more attractive to private investors.

The evaluation of warranty contracts is of great interest to highway agencies that seek innovative ways to manage their assets in the most cost-effective way. This paper demonstrates a statistical approach for evaluating the long-term performance and cost effectiveness of pavement warranties via comparison with similar traditional contracts. This is in contrast to past studies that used descriptive statistics and one-to-one comparisons to evaluate the performance of these two contracting approaches. In this study, random-parameter regression models that account for unobserved heterogeneity were developed to evaluate the performance of warranty and traditional contracts for similar projects on the basis of resulting pavement service life. The projects studied involved structural hot-mix asphalt overlay of crack-and-seat rigid pavements. The warranty projects in the study data sample were found to be 10% more effective in terms of average service life compared with traditional contract projects. Additionally, routine maintenance expenditures after the warranty period were up to 194% higher for traditional contract projects. These results suggest that warranty contracts have higher initial and rest-of-life quality. However, in terms of cost effectiveness (herein defined as the ratio of condition or longevity to lifecycle cost), the results suggest that the traditional contracts in the sample were 6–15% more cost-effective, on average, compared with their warranty counterparts, which could be attributed to warranty contracts’ higher costs.

This paper presents a replicable framework for geographic information system–(GIS) based land suitability analysis of utility-scale wind farms using publicly available and permissible data. The framework identifies 10 suitability factors accounting for environmental, ecological, cultural, human settlement, physical infrastructure, and wind resource aspects. It uses fuzzy-based nonlinear membership functions for scoring and the analytic hierarchy process (AHP) for weighting by engaging a group of experts. For the case study state, Indiana, suitable lands are identified and compared with existing wind farm developments. A sensitivity analysis shows suitable locations for wind farm development assuming different factors-weighting scenarios, such as suitable locations without considering environmental and ecological aspects. Such state-level analysis can be helpful for permitting agencies, local planners, and engineers engaged in permitting processes, zoning ordinances, and the development of regulations for the wind farms.

To address the integrity concerns related to aging pipelines, this paper uses reliability analysis to evaluate the time-dependent performance of pipeline subjected to external corrosion considering prevailing uncertainties. A power-law function of time model is proposed to probabilistically predict the growth of corrosion maximum defect depth and defect length considering a Poisson process for the occurrence of defects. This model can be used: (1) when either matched or nonmatched defects are available; and (2) to consider the newly generated defects since the last inspection. The Bayesian methodology is employed to assess the unknown model parameters using the in-line inspection data and a bivariant normal distribution is adopted to construct the likelihood function with the consideration of the dependency of defect depth and length growth models. The performance of the pipeline is evaluated through assessing probability of failure per kilometer, which is defined as a series system of detected and newly generated defects within that kilometer for small leak, large leak, and rupture failure modes. Sensitivity analysis is also performed to determine to which parameter(s) the reliability of the studied pipeline is most sensitive.

Of all roadway vehicles, trucks inflict the greatest deterioration to pavements and bridges owing to their heavy gross weights and axle loads. States issue permits to trucks beyond legal weight limits and collect fees to compensate for additional damage. To study the extent to which state departments of transportation (DOTs) have allowed passage of overweight loads, the first objective of this paper was to characterize overweight load permit practices among all U.S. states, and the second objective was to identify stakeholders’ perspectives on how to modernize current overweight permit practices. Through an analysis of existing fee policies, this research has characterized the state of the practice in permit fees for overweight loads on public roadways, and evaluated these practices. The subsequent data showed a wide array of policies on overweight permitting, such that a single interstate overweight freight trip might encounter several diverse overweight permitting policies. Although the range of weights permitted with overweight permits can lead to substantial infrastructure damage, the median user-fee for a flat-rate annual overweight permit was only $250 without any limit on maximum allowable number of trips. In this paper, the authors discuss permitting structures, the prevalence of each type of structure in the United States, and the values assigned to permits. In practice, policymakers, rather than engineering and economic studies, have usually set fees. Several interviews were conducted in South Carolina as a case study to assess stakeholders’ perspectives on overweight truck fee policies in the state. South Carolina charged $30 for a single-trip permit and $100 for an annual permit at the time of the study. Overweight policies among all states showed no clear or consistent relationship between the cost of an annual permit and the fee imposed for a single trip. For example, the South Carolina annual permit fee equated to 3.33 times the single-trip permit fee, whereas an Ohio study found that annual permit holders made an average of 24.8 trips with annual permits. Policy makers should evaluate the number of trips and miles that annual-permit holders incur before establishing annual permit fees. A recent study in South Carolina found that damage costs for loads exceeding highway standards were higher than overweight fees covered. Interviews with stakeholders showed that, fundamentally, South Carolina’s trucking stakeholders did not hold common ideas on the objective of overweight permits and fee structures. South Carolina will not likely find fee revisions and increases politically viable until a consensus develops around the objective of overweight permitting and fees. The South Carolina DOT and trucking industry representatives should work together in an ongoing focus group to develop common understanding of issues, consensus around objectives, and provisions for fairness that will address industry concerns. This paper’s findings on South Carolina stakeholders’ perspectives will provide input to other regions in the country that are considering revising overweight trucking fees.