Journal of Infrastructure Systems

An increase in the number of extreme weather events and gradual shifts in climate parameters due to a changing climate pose a serious threat to the nation’s roadway infrastructure. A systematic approach is needed to define risks and assess consequences of climate change, consider the uncertainties, rank priorities, and initiate an adaptation strategy in a cost-effective manner. The objective of this study is to develop a framework that could be used to assess the impact of climate change on pavements in a rational way using either the net present value (NPV) or the real option (RO) approach to compare several options and to make the most prudent decision regarding selecting an option and the time of adopting that option. The NPV approach will generally go against an investment in cases with high uncertainty, even if they are very promising, and does not take into account the flexibility or decisions that could be implemented on the basis of changing conditions. In contrast, the RO method offers a flexible deferment option when the uncertainties regarding outcomes are resolved to a certain extent. A framework with a step-by-step method for evaluating the feasibility of building roads that are resilient to a changing climate is presented, along with an example. The worked-out example shows that there could be considerable value in using RO analysis, and this value can be leveraged to develop better economic policies for building roads that are resilient to a changing climate.

Data on the quality of small-scale infrastructure assets at the neighborhood level tend to be scarce, delayed, or even nonexistent, limiting the ability of local communities to better manage assets, identify risks, and improve performance. Participatory methods, common in ecology and environmental science, provide an opportunity for producing these fine data. However, very little is known about the validity and reliability of infrastructure data collected by volunteer members of the public. In this paper, we examine the validity and reliability of local drainage infrastructure monitoring data obtained from citizen scientists. The volunteer citizen scientists were high school students collecting data about the condition of stormwater infrastructure in their local community. The study took place in an environmental justice neighborhood in Houston with a long history of flooding and exposure to environmental hazards. The results suggest that with adequate planning, training, and organized community engagement efforts, the validity and reliability of data collected by citizen scientists can be comparable to physical measurements and data obtained from trained inspectors.

Roadway pavement maintenance to the preferred level of serviceability comprises one of the most challenging problems faced by civil and transportation engineers, with regard to transport infrastructure management. This paper presents a study on the detection of roadway pavement anomalies by use of smartphone sensors and on-board diagnostic (OBD-II) devices, which can lead to low-cost roadway infrastructure assessment. The proposed approach, which, in addition to smartphone sensors, also utilizes artificial neural network (ANN) techniques in the analysis, captures a vehicle’s interaction with a roadway pavement while the vehicle is moving, and utilizes the observed interaction patterns for the detection of potholes in the pavement. The method utilizes four metrics in the analysis and shows a detection accuracy of about 90%. Preliminary results on the inclusion of additional roadway defects in the analysis and on the ability of the method to distinguish between potholes and other pavement defects (e.g., patches, local upheavals, rutting, and corrugation) have been positive. The study’s results confirm the value of smartphone sensors in the low-cost (and eventually crowdsourced) detection of potholes.

Rapid investigation and damage assessment are crucial for cultural heritage conservation. At present, mobile crowd sensing (MCS) techniques are very effective for cultural heritage investigation and data collection. Unfortunately, data collected based on MCS techniques cannot be fully utilized and analyzed. To overcome this limitation, this study combines MCS techniques and a state-of-the-art deep learning algorithm to realize rapid investigation and damage detection of the Great Wall in China. The GreatWatcher system, based on MCS techniques and a deep learning algorithm, was developed in this study, focusing on big data collection and damage detection for the Great Wall. The system highlights the significance and emerging revolution of the combination MCS techniques with deep learning methods in the cultural heritage field. System components include a mobile client (data collection), web platform (data storage database), and computing terminal (data analysis and automatic damage detection). Two field investigations and data collection for the Great Wall were performed to verify the feasibility and effectiveness of the system. Based on the collected data, a deep learning method was used to automatically analyze damage to the Great Wall at the computing terminal. Moreover, various validation experiments of different conditions were performed to verify the good performance of the deep learning method.

Several deterioration models have been developed for prediction of the actual and future condition states of individual sewer pipes. However, most tools that have been developed assume a linear dependency between the predictor and the structural condition response. Moreover, unobserved variables are not included in the models. Physical processes, such as the deterioration of pipes, are complex, and a nonlinear dependency between the covariates and the condition of the pipes is more realistic. This study applied a Bayesian geoadditive regression model to predict sewer pipe deterioration scores from a set of predictors categorized as physical, maintenance, and environmental data. The first and second groups of covariates were allowed to affect the response variables linearly and nonlinearly. However, the third group of data was represented by a surrogate variable to account for unobserved covariates and their interactions. Data uncertainty was captured by the Bayesian representation of the P-splines smooth functions. Additionally, the effects of unobserved covariates are analyzed at two levels including the structured level that globally considers a possible dependency between the deterioration pattern of pipes in the neighborhood and the unstructured level that account for local heterogeneities. The model formulation is general and is applicable to both inspected and uninspected pipes. The tool developed is an important decision support tool for urban water utility managers in their prioritization of inspection, maintenance, and replacement.

Political risk is a recognized deterrent to foreign direct investment in infrastructure (FDII) in developing countries. The sensitivity of FDII to political risk is amplified above that of common foreign direct investment (FDI) as a consequence of the distinguishing characteristics of FDII: larger capital commitments, longer payback periods, and greater dependency on government stability. Moreover, developing countries experience political risk at higher levels than developed countries. Nevertheless, the literature remains conflicted as to the exact impact of political risk on FDI and infrastructure investment. Some studies reported the impact of political risk as considerable, whereas others reported it as marginal when corrected for other factors. This study seeks to resolve the uncertainty surrounding the effect of political risk on FDII. It draws on a database of 90 developing countries from 2006 to 2015. The hierarchical cluster method and general least-squares regression are applied to analyze this database against further FDII moderators identified in the literature: gross domestic product (GDP), GDP per capita, GDP growth rates, macroeconomic conditions, and infrastructure stock, as well as 12 political risk indicators. Findings reveal that in about half of the countries, FDII is indeed sensitive to political risk, whereas in the other half, it is less sensitive. Moderating these outcomes are economic factors. In countries with higher GDP but lower GDP growth and higher stocks of infrastructure, political risk plays a stronger role in deterring FDII. However, when countries have lower GDP but higher GDP growth and lower stocks of infrastructure, political risk plays a lesser deterrent role in FDII. The identification of this duality of developing country infrastructure investment profiles will be of practical interest to both capital investors and those seeking investment.

This paper presents findings of a recent study that was conducted in Canada on the quantification of pavement sustainability. The ultimate goal of this study was to develop a framework and explore the use of multicriteria decision-making techniques to formally assess the sustainability of pavement engineering alternatives. While sustainability is of increasing concern in pavement engineering, environmental performance is rarely used by pavement managers to select maintenance practices. There is therefore a need to develop a framework for the practical consideration of environmental effects in pavement management. This paper aims to provide a better understanding on the use of multicriteria decision-making techniques based on hierarchy process (AHP) and choosing by advantages (CBA) for the integration of sustainable aspects in the decision-making process of pavement management. A case study comparing pavement maintenance technologies using cold-in-place recycling and traditional solutions based on mill and overlay is analyzed for illustrative purposes. Results obtained using both multicriteria techniques are compared, including a sensitivity analysis on the importance of sustainability criteria in the evaluation of maintenance alternatives. Results obtained from this case study show that AHP and CBA provide consistent recommendations in which cold-in-place technologies are preferred over traditional alternatives. However, CBA presents the advantage of separating cost from the analysis, letting the agency to decide whether they are willing to pay more to use more sustainable alternatives. This finding has significant implications for engineering practice, given that AHP is widely used not only in the pavement field but in infrastructure management. Further research is needed to incorporate social aspects and existing barriers for the implementation of sustainable technologies in the proposed sustainability evaluation.

This paper addresses the problem of the optimal coastal protection area against sea level rise by utilizing fine-grained homogeneous segments, namely, operational landscape units (OLUs). The approach is demonstrated through a case study application focused on San Mateo County and Santa Clara County in the San Francisco Bay Area. We use the Coastal Storm Modelling System (CoSMoS) for detailed predictions of coastal flooding and inundation of bay–shore segments. The result of these hydrodynamic interactions leads to transportation network disruptions that, in turn, lead to changes in traffic flow patterns. Specifically, under a 0.5-m sea level rise scenario that is expected to occur in 2054, we forecast transportation network disruptions due to the inundation from the sea level rise and assess the impacts of protecting OLUs in the two counties of interest in terms of travel time delay reduction over the entire San Francisco Bay Area. We use agent-based traffic simulation (MATSim) with a daily activity list for 500,000 commuters. Finally, our results identify the most critical OLUs in San Mateo County and Santa Clara County. We conclude that the optimal coastal segment protection strategies depend strongly on the hydrodynamic interactions between neighboring counties and OLUs and on the traffic flow patterns after inundations. In forthcoming studies, long-term land use pattern change should be considered to establish protection strategies of coastal areas.

Asset management is a strategic tool for maintaining the value of an asset at a level that is satisfactory for users, asset owners, and taxpayers. This paper introduces the concept of service value. The concept is applied following the recommendations of the Organisation for Economic Co-operation and Development (OECD): service value equals the value of economic benefits generated by an asset. It is shown how alternative value accounting methods result in different asset values. The present value method, as recommended by accounting practitioners and in line with the concept of service value, is more appropriate for asset management purposes than the perpetual inventory method (PIM), despite the fact that the latter is more widely used. The impact of correct asset and service value accounting extends well beyond project and investment program management levels to the national accounting system of fixed capital. Incorrect asset management accounting may lead to serious under- or overestimation of investments and repair debt.

This paper’s objective is to determine the usual typologies of deficiencies in external construction elements, identifying the most frequently recurring anomalies by analyzing all judicial complaints pertaining to construction in Spain between the years 2008 and 2010. A total of 368 anomalies were identified in 1,166 records of judicial complaints related to external construction elements. The types of damages with the highest number of cases are settling (22.55%) and bulging and/or tenting of floor cladding (20.38%). The causes with the highest prevalence are deficient ground compaction (26.36%) and characteristics of the material or of its finishing (16.03%). The construction elements in which anomalies arise most frequently are outdoor flooring and perimeter footpaths, representing 67.93% of the total. The most frequent cause-damage relationship is that of settling caused by deficient ground compaction. The methodology used and the results obtained through this research contribute to optimizing construction quality, allowing designers and site managers to easily detect the most common damages along with their causes. As a result, they will be able to act with an awareness of the origin of problems and avoid issues in other situations.

Pavement-management systems are important tools that planning agencies depend on to cost-effectively maintain their roadway systems. The incorporation of uncertainty (beyond that related to pavement degradation) within these systems is limited, causing decision-makers to be unable to (a) recognize the risk of their status quo maintenance policy and (b) appreciate the value of embedding flexibility to capitalize on a prosperous future or mitigate downside risk. Consequently, this research develops a stochastic simulation model for pavement management that accounts for uncertainty as it relates to both pavement deterioration and the future cost of maintenance actions. The stochastic simulation model includes an allocation algorithm for sequential decision-making for the large-scale, performance maximization problem that generally leads to a high-fidelity solution. The authors implement the full model in a real-world case study in which they evaluate the implication of a planning agency broadening the types of paving materials and designs that it uses to proactively deal with an uncertain future. While the deterministic model estimates that an agency can achieve its objectives at a 4% cost reduction using a broader range of technologies, the probabilistic model estimates an expected cost reduction of 10%. This difference stems from the ability of the probabilistic model to capture the value generated from the flexibility to alter investment strategies at moments of spiraling costs for some maintenance actions and suppressed price levels for others. These findings suggest that the benefit from incorporating a diverse range of paving materials and designs by a planning agency could be much higher than agencies realize using the current deterministic frameworks.

Tree roots can cause damage to surface and subsurface infrastructure. Hence, timely detection of root system architecture (RSA) is needed to reduce conflict between trees and man-made facilities. Because excavation is expensive and often restricted, noninvasive detection of RSA by ground penetrating radar (GPR) is a promising technique. Although several studies have proven the ability of GPR for RSA detection, the problem of distinguishing roots from unwanted reflections at urban test sites with heterogeneous, silty, clayey, or stony soil has not yet been fully solved. This study assessed the performance of GPR for in situ detection of RSA from a plane tree (Platanus acerifolia) and a buckeye (Aesculus hippocastanum) in urban heterogeneous multilayered soil using shielded 250-MHz antennas. Repeated manual hyperbola selections were performed, extracting the three-dimensional (3D) coordinates, which were visualized in top view to reveal connected structures. Unwanted selections were manually filtered by internal confirmation using depth slices from 3D radargram interpolations. Root indications were retraced in the field and validated by vacuum excavation. At our test site, the suggested approach was suitable for detecting the lateral positions of roots with diameters between 1 and 4 cm at depths of 17 to 70 cm, despite unfavorable substrate. Moreover, the assumed depth ranges were correct for both trees, and the main depth characteristics were fairly precisely projected. The rapid and cost-effective protocol allows minimal interventions and opens the door for similar applications in urban and nonurban land uses.