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.

Investments and policies for transportation infrastructure projects are influenced and possibly undermined by diverse stakeholders with limited incentive for coordination. Effective management of such systems requires agencies to prioritize capacity projects with consideration of potential stakeholder-divergence resulting from competing stakeholder values and budgetary constraints. Strategic prioritization of investment should consider the values of several stakeholder perspectives, such as managing agencies, the public, closely associated commerce, environmental agencies, and others. This paper will demonstrate an integrated scenario analysis, multicriteria analysis, and integer-programming approach to identify investment portfolios that meet budgetary constraints while promoting investments most suitable for coordination among diverse stakeholders. The approach will be demonstrated on six high-priority federal transportation infrastructure projects in the United States. The results illustrate the need and use of stakeholder-divergence as a factor in federal investment policies. The methods result in proactive identification of dissent or progressive issues early in the project planning process, potentially avoiding postimplementation regret.

Investments in transportation projects are typically justified by diverse potential benefits, including safety, environmental, energy savings, congestion mitigation, and others. There is a need for such benefits to be comparable early in transportation programming, to allocate scarce resources to preliminary engineering. This paper discusses quantitative methods to aid in prioritizing locations of future highway projects. The paper adopts 15 quantitative metrics including crash rate, emergency route access, environmental issues, level of service (LOS), volume-to-capacity ratio, traffic flow, intermodal access, heavy truck usage, unemployment rate, right-of-way use, use of alternative transportation modes, bridge sufficiency rating, and cost effectiveness. This effort contributes to real-world transportation programming and priority setting via analysis of the crash avoidance and other benefits and costs that are expected before project designs are available. The innovation of this paper is in two areas: (1) estimation of broad-spectrum benefits across several categories including crashes avoided, travel time saved, fuel uses avoided, and emissions avoided; and (2) development of a software tool that compares project benefits to prefeasibility estimates of project costs. The developed software represents the uncertainty of the results using numerical intervals. The paper illustrates how project selection is informed with sparse and/or early information on a large number of potential locations and needs. With interval analysis of uncertainty, decision makers might conclude that existing evidence is sufficient to distinguish among competing needs. The methods are demonstrated for 53 project locations ranging in their prefeasibility cost estimates from $2 million to $130 million.

The Road Weather Information System (RWIS) has been applied as an effective tool to collect weather and road surface temperature information and to assist operational decisions for snow and ice control activities. Current practices on siting regional RWIS stations rely on the knowledge and experiences of maintenance and operation personnel, which is subjective and inefficient. A two-stage sequential model is developed in this paper. The Stage-1 model optimizes the number and locations of RWIS stations, which intends to maximize spatial coverage with the least overlapping area, considering the variation of regional weather severity. The deployment of the optimized RWIS sites can be prioritized by the Stage-2 model, which maximizes the lifecycle profit subject to limited budget. The proposed methodology is applied to optimize a RWIS network for the State of New York.

Productivity and project performance can be improved through implementing best practices. This paper describes the development of a best productivity practices implementation index (BPPII) for infrastructure projects. The index is a checklist of practices that are considered to have a positive influence on labor productivity at the project level for infrastructure projects. These practices have been grouped together into a formalized set of categories, sections, and elements. Each practice and its planning and implementation levels were defined and assigned a relative weight on the basis of its importance in affecting labor productivity. The productivity factor (PF), defined as a ratio of estimated productivity and actual productivity, was used as a metric to collect information about labor productivity to validate the accuracy of the BPPII for infrastructure projects. Data were collected for infrastructure projects in regards to their planning and implementation level of practices in addition to their PF and project schedule performance. The statistical tests confirmed that the higher implementation of best practices as defined in the index have a strong positive relationship with the PF and project schedule performance. Projects that have a high level of implementation of practices experienced better productivity and schedule performance than those having low implementation. This research contributes new insight into the relationships between sets of practices and project performance as well as a tool for planning practice implementation on infrastructure projects.

The current study approaches the issue of deteriorating civil infrastructure components in a systematic manner through microscale, mesoscale, and macroscale investigations. At the microscale, the integrity of individual network components is examined considering the extent of deterioration due to aggressive environmental conditions and climatic effects. For this purpose, the deterioration of structural members is evaluated through a detailed finite-element framework and the corresponding structural models are updated at regular time intervals to capture the effects of aging mechanisms. To quantify the vulnerability of deteriorating components subjected to earthquake events, a series of nonlinear time-history analyses are performed and time-dependent seismic fragility curves are generated. Based on the state of damage in the network components under a set of scenario earthquakes, appropriate damage indexes are introduced to measure the postevent functionality of the network at the mesoscale. Through the study of a full-scale transportation network that serves a large metropolitan area, an integrated traffic assignment model is then developed to estimate the travel time in the network before and after an earthquake event. At the macroscale, the analyses go above and beyond the components of the transportation network and provide a holistic approach to calculate the losses associated with disruptions in deteriorating networks due to extreme events.

State highway agencies need pavement-condition data to select candidates for pavement maintenance and rehabilitation. However, it is a challenge for pavement engineers to simultaneously assess a number of attributes that represent different aspects of pavement condition. In conventional practice, empirical comprehensive evaluation methodologies, such as fuzzy set theory and analytical hierarchy process, have been used to aggregate multiple distresses into integrated pavement-performance indices. These methodologies, however, are mostly based on experts’ or engineers’ judgment rather than data-driven approaches. In this paper, a framework of applying a data-driven approach to conduct comprehensive pavement evaluation and ranking is presented. The method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is introduced and applied to rank pavement sections with various evaluation attributes. Principal component analysis (PCA) is employed to combine distresses with high correlations and reduce the data dimension. A case study using data from a pavement management system in Lincoln Parish, Louisiana, is presented to demonstrate the feasibility and effectiveness of the proposed methodology. A total of 18 parameters involving four aspects of pavement condition, surface distress, roughness, safety characteristic, and structural capacity, are analyzed to rank the 35 pavement sections. Discussions and recommendations are presented.

For the purposes of long-term planning and budgeting, infrastructure user cost allocation, and financial need forecasts, infrastructure agencies seek knowledge of the annual expenditure levels for maintaining their assets. Often, this information is expressed in dollars per unit dimension of the infrastructure and is estimated using observed data from historical records. This paper presents an artificial neural network (ANN) approach for purposes of estimating annual expenditures on infrastructure maintenance and demonstrates the application of the approach using a case study involving rural interstate highway pavements. The results of this exploratory study demonstrate that not only is it feasible to use ANN to derive reliable predictions of annual maintenance expenditures (AMEX) at aggregate level, but also it is possible to identify the influential factors of such expenditures and to quantify the sensitivity of AMEX to such factors.

On the basis of time-dependent reliability analysis, a computational framework called subset simulation (SS) has been applied for risk-cost optimization of flexible underground pipeline networks. SS can provide better resolution for rare failure events that are commonly encountered in pipeline engineering applications. Attention in this work is devoted to scrutinize the robustness of SS in risk-cost optimization of pipelines. SS is first employed to estimate the reliability of flexible underground pipes subjected to externally applied loading and material corrosion. Then SS is extended to determine the intervention year for maintenance and to identify the most appropriate renewal solution and renewal priority by minimizing the risk of failure and whole life-cycle cost. The efficiency of SS compared to genetic algorithm has been demonstrated by numerical studies with a view to prevent unexpected failure of flexible pipes at minimal cost by prioritizing maintenance based on failure severity and system reliability. This paper shows that SS is a more robust method in the decision-making process of reliability-based management for underground pipeline networks.

This paper presents an optimization model for scheduling railway ballast, rail, and sleeper renewal operations at a network level. The objective of the model is to minimize the expected railway track life-cycle cost (LCC) and track unavailability costs derived from user impacts caused by traffic disruption during track renewal operations. To minimize costs, the model assesses the opportunistic renewal of railway track components and takes advantage of planning from a network perspective to study the possibility of reusing track components on different lines. The practical utility of the model is illustrated with a case study involving the Portuguese railway network. The results indicate that user costs have an important influence on decision making in the track renewal process and that the network perspective of renewal planning can reduce the direct costs of these operations.

Reliability-based analysis of the calibration of pullout resistance factors (${\phi }_{PO}$) for soil nails are presented in this paper. To conduct the calibration, a total of 47 verification tests conducted in cohesive soil within the Lyndon B. Johnson (LBJ) Express construction project in North Dallas Texas were collected. Three failure criteria, i.e., creep test data, test curve evaluation, and load-elongation curve were selected to estimate the nominal bond resistance between soil and grout. As a result, 25 tests met at least one of the failure criteria. In addition, 45 existing test results from the National Cooperative Highway Research Program (NCHRP) Report 701 in cohesive soil were incorporated to the test database and resulted in a total of 92 soil nail tests including 22 nonfailed tests. These 92 tests were separated into three databases for analysis and calibration purposes. Statistical analysis was conducted on these databases to evaluate the predicted ($R_p$) to measured ($R_m$) nominal bond resistances and normal and lognormal distributions were conservatively fitted to the lower tail of the failed test data. A new survival analysis approach was proposed to analyze the resistance distribution, which allows for incorporation of the nonfailed verification testing results. Utilizing these distributions, calibration of ${\phi }_{PO}$ was conducted by load and resistance factor design (LRFD) reliability analysis with the strength limit state and Monte Carlo simulations. A target reliability index (${\beta }_T$) of 2.33 and load factors (${\gamma }_Q$) of 1.0, 1.35, 1.5, 1.6, and 1.75 were selected for the calibration as a result of the redundancy and loading conditions commonly found in soil nail walls (SNWs). From this analysis, $65{\phi }_{PO}$ values were calibrated and ranged from 0.42 to 1.49, allowing engineers to select a ${\phi }_{PO}$ value appropriate to the SNW characterizations and conditions in cohesive soil.

Projects are considered interrelated when their benefits or costs depend on which other projects are implemented. The timing of such projects may also complicate their analysis. Selection and scheduling of interrelated projects is a challenging optimization problem that has many applications in various fields, including economics, operations research, business, management, and transportation. The goal is to determine which projects should be selected and when they should be funded in order to minimize the total system cost over a planning horizon. Finding the optimal solution for such problems often requires extensive evaluation of possible solutions because of the complex nature and noisy surface of their solution space. This paper applies three metaheuristic algorithms including a genetic algorithm (GA), simulated annealing (SA), and Tabu search (TS) in seeking efficient and consistent solutions to the selection and scheduling problem. These approaches are applied to a special case of link capacity expansion projects to showcase their functionality and compare their performance. The paper’s main contributions are to (1) compare three metaheuristics for this problem in terms of solution quality, computation time, and consistency; (2) consider explicitly the supplier costs as well as user costs in the formulated objective function; and (3) enhance some simplifying assumptions from previous studies by recognizing that candidate projects may not remain economically justifiable throughout the analyzed period. It is found that a GA yields the most consistent solution with the least total cost while SA and TS approaches excel in terms of computation time.

Public private partnerships (PPPs) have been adopted widely to provide public facilities and services. According to the PPP agreement, PPP projects would be transferred to the public sector. However, problems related to the subsequent management of ongoing PPP projects have not been studied thoroughly. Residual value risk (RVR) can occur if the public sector cannot obtain the project in the desired conditions as required in the agreement when a project is being transferred. RVR has been identified as an important risk in PPPs and has greatly influenced the outputs of the projects. In order to further observe the change of residual value (RV) during the process of PPP projects and to reveal the internal mechanism for reducing the RVR, a comparative case study of two PPP projects in mainland China and Hong Kong was conducted. Based on the case study, different factors leading to RVR and a series of key risk indicators (KRIs) were identified. The comparison demonstrates that RVR is an important risk that could influence the success of PPP projects. The cumulative effects during the concession period can play significant roles in the occurrence of RVR. Additionally, the cumulative effects in different cases can make the RVR different because of different stakeholders’ efforts on the projects and ways to treat RVR. Finally, alternatives for the public sector to treat RVR were proposed. The findings of this research can reduce RVR and improve the performance of PPP projects.

Geotechnical assets are those found along transportation environments and are made up of or supported by earth materials. A geotechnical asset management program aims to achieve life-cycle performance goals by maintaining the geotechnical assets in a safe, cost-effective, and timely manner. Interferometric synthetic aperture radar, a remote sensing technique that derives ground deformation information from satellite-based radar data, is proposed as a monitoring method in the context of geotechnical asset management. A total of 90 ERS-1, ERS-2, and ENVISAT radar images, acquired from 1992 through 2010, are processed using two interferometric stacking techniques to measure ground deformation along a railroad corridor. A local-scale study was first conducted over a known slide (S-1) where total displacements up to 6 cm were measured. Then a regional-scale study was performed to locate hazard zones. Six potential geo-hazard zones were identified along the railroad corridor based on five criteria: slope distance, slope height, slope angle, average downslope velocity, and total downslope displacement. Interferometric stacking is shown to be quite useful for geotechnical asset management, as this remote sensing technique allows for an indirect method of quantifying asset condition and allows for the long-term monitoring of geotechnical assets that a management program aims to achieve.