Seven-Layer Classification of Infrastructure to Improve Community Resilience to Disasters
Abstracts:Community resilience to natural disasters depends to a large extent on the adequacy of infrastructure services provided through multidimensional interdependent systems during postdisaster recovery. In the aftermath of a disaster, enormous and diverse community recovery needs emerge, necessitating an effective interplay of cyber-physical-social systems during emergencies and throughout the phases of short- and long-term recovery. With recognition of the important roles played by multidimensional infrastructure, considerable research and development has been undertaken in each discipline to support communities in the aftermath of disasters. These multiple domains of infrastructure systems are heterogeneous in their structure and operation but are nevertheless interrelated. Therefore, the absence of an integrated platform on which emergency personnel from different disciplines could coordinate resilience planning often hampers the smooth and effective recovery process of communities during postdisaster recovery. This study proposes a new approach for analyzing capacity needs of a wide range of critical infrastructure during postdisaster recovery, framed within seven interrelated infrastructure layers: civil, civic, social, financial, environmental, educational, and cyber/communication. This research employs node analysis to deal with the complexity of the infrastructure layer networks and visualize the interplay among them. The proposed seven-layer framework facilitates an understanding of the interdependencies within and between the infrastructure layers, and an analysis of various communities’ needs specific to the phases of postdisaster recovery. Finally, the implementation and the benefits of this seven-layer conceptualization are demonstrated through case studies of past postdisaster recovery operations.
Thematic Overview of Corruption in Infrastructure Procurement Process
Abstracts: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.
How Spatial and Functional Dependencies between Operations and Infrastructure Leads to Resilient Recovery
Abstracts:A fast recovery of infrastructure functioning is important to the well-being of residents and the economy following interruption or disaster. Assessing the chances of recovery, or creating plans to enable it, is difficult due to the many interactions between components and operations. From a modeling perspective, addressing this challenging problem requires a capability for constructing representations of the dynamic interactions between elements that addresses how hazard and failure effects cascade and how recovery efforts propagate. Here, a geospatial resilience assessment platform is proposed containing a modeling approach comprising the capabilities necessary to address the challenge of urban infrastructure and operation recovery assessment and planning. It is designed to be reusable and integrate with reusable damage assessment tools such as Hazus. The approach combines a novel means to construct geospatial dependency models that can assess element-by-element recovery over time through integration with a computational recovery assessment engine called the graph model for operational resilience. A sample model and assessment that illustrates recovery time assessment of infrastructure services to the buildings of a neighborhood subject to varying infrastructure failures are provided. The case provides indications of the degree of burden on emergency management sustainment resources that may exist and how risk treatments can improve recovery times. In particular, the impact of the order of component recovery is examined in a multi-infrastructure setting.
Relationships among Value-for-Money Drivers of Public–Private Partnership Infrastructure Projects
Abstracts:Value-for-money (VFM) drivers and their interrelationships have large impacts on increasing the VFM of public–private partnership (PPP) projects. However, very few studies have explored the potential interrelationships among VFM drivers. Using a questionnaire survey and structural equation modeling (SEM), the current study aims (1) to refine a VFM driver framework for PPP projects; and (2) to determine and verify the interrelationships among VFM drivers for PPP project implementation. This research has observed that the determinants of cost and effectiveness are provided for financial or performance sustainability. Moreover the better the overall cooperative environment, the better the participants’ capabilities and characteristics and the more effective the cooperation between the public and private sectors. Furthermore, the level of cooperation favorably influences cost and effectiveness directly. Finally, a stable macroeconomic condition has the most significant impact on the cooperative environment. The determined interrelationships among VFM drivers will contribute to the increased sustainability and successful delivery of PPP projects.
Deterioration and Predictive Condition Modeling of Concrete Bridge Decks Based on Data from Periodic NDE Surveys
Abstracts:A novel approach and program are developed for deterioration and predictive modeling of concrete bridge decks based on nondestructive evaluation (NDE) data. Through an iterative process—combined with data processing, bridge deck segmentation, regression analysis, data integration, and deterioration and predictive modeling—the developed program aids estimates of the remaining service life of bridge decks. Data collected on an actual bridge deck during a period of five and half years are used to illustrate the operation and performance of the developed program. Based on evaluation of condition maps, condition indices, and deterioration curves developed for a range of input parameters, the proposed method quantifies progression of deterioration in bridge deck. By reviewing the predictive models, combined with segmentation of the bridge deck area, a more realistic and practical estimation of the deck’s remaining service life can be made. It is anticipated that the proposed method will provide objective and comprehensive evaluation and prediction of bridge deck condition based on data from multiple NDE technologies.
Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks
Abstracts:The replacement of deteriorating distribution pipes is an important process for water utilities. It helps reduce capital spending on water main breaks and improves customer satisfaction. To assist with the development of an effective renewal plan, statistical models that forecast future breakage rates have been used to guide planning for asset management. However, this process is difficult for older utilities that lack readily available pipe network data. We examined whether accurate and useful predictive models can be built in the absence of pipe-feature data. Using the historical break record from a mid-Atlantic utility, two data sets at different spatial scales were created using publicly available demographic and environmental information. Empirical results suggest that although accuracy suffers from the lack of pipe-level details, it is still possible to create a model that provides useful information for prioritization of high-risk regions for management.
Factors Affecting the Accuracy and Variability of Pavement Surface Evaluations and Ratings
Abstracts:This research identified factors influencing the accuracy and variability of pavement surface evaluations and ratings (PASER). Survey and PASER data obtained from workshop participants were used to develop statistical models estimating three definitions of rater accuracy: (1) bias, which is the absolute value of the difference between a participant’s rating and the segment’s true rating as determined and agreed upon by PASER-certified instructors; (2) average participant bias and bias standard deviation; and (3) good/fair/poor category identification. The results indicate that pavement in good condition was rated more accurately, while pavement on the poor/fair condition boundary was rated less accurately. Participants were more accurate in assigning PASER ratings after receiving PASER-specific training. Additionally, raters that were more accurate were also more consistent in performing PASER ratings. Participants with engineering roles, such as engineer, engineer technician, or engineer assistant, were more accurate in assigning PASER ratings. In contrast, participants with leadership roles, such as supervisor, manager/foreman, or team leader/elected official, or less than 1 year of PASER rating experience were less accurate in assigning PASER ratings.
Temporal Assessment of the Embodied Greenhouse Gas Emissions of a Toronto Streetcar Line
Abstracts:Transportation greenhouse gas (GHG) emissions often account for the largest share of urban GHG emissions. Consequently, large-scale reductions in urban GHG emissions will not be possible without significant improvements in the transport sector. Increasing public transit mode share is widely promoted in efforts to reduce GHG emissions from transport. Large increases in public transit use will require the provision of new transportation infrastructure, which is itself GHG intensive. This paper presents a time-dependent analysis of the embodied GHG emissions associated with construction and reconstruction for the refurbishment and street redesign of the 510 Spadina streetcar route in Toronto, Canada during a 38-year period. From 1987 to 2015, the embodied emissions in the line’s civil infrastructure are calculated as 27.4 kilotons of
). It is expected that, by 2025, further reconstruction of the right-of-way (ROW) will increase embodied GHG emissions to
. Overall, reconstruction projects increase GHG emissions by 25.9% beyond initial construction. When accounting only for at-grade infrastructure, reconstruction increases embodied emissions by 45.8% during the 38-year study period.
Deep Learning for Critical Infrastructure Resilience
Abstracts:Ensuring the resiliency of critical infrastructures is essential in modern society, but much of the deployed infrastructure has yet to fully leverage modern technical developments. This paper intersects two unique fields—deep learning and critical infrastructure protection—and illustrates how deep learning can improve resiliency within the electricity sector. Machine vision is the combination of machine intelligence, or computer systems automatically learning patterns from exemplar data, and image analysis, or objects of interest being automatically segmented and identified from video image data. This technology has the potential to automate threat assessments in the context of securing critical infrastructures. Rather than traditional reactionary approaches, we present here a method of leveraging deep learning for the detection of threats to critical infrastructures before failures occur. This paper discusses the state-of-the-art in deep learning for creating machine vision systems, and the concepts are applied to increase the resiliency of critical infrastructures. The intersection between machine vision and critical infrastructures is discussed, as are key benefits and challenges of invoking such an approach, and examples within several fields of critical infrastructures are presented. Automated inspection of the power infrastructure using vehicle-mounted video acquisition equipment is explored, and a proof-of-concept implementation of a deep convolutional neural network is developed, achieving 95.5% accuracy in distinguishing power-related infrastructures within images largely typical of rural settings. These preliminary results show promise in the application of deep learning and machine vision to protecting critical infrastructures through preventative maintenance.
Impact of Side Friction on Performance of Rural Highways in India
Abstracts:Abutting land-use patterns always play an important role in the performance of a road section. Land use along rural highways in developing countries is significantly different from that in developed countries. Existence of side friction in rural highways is a common characteristic in developing countries like India. In India, substantial investment has been made to improve the performance of various categories of roads. Widening and strengthening projects have been taken up and condition of road surfaces has been improved, but travel speeds and service levels have not been improved as expected because of prevailing land-use patterns. One important factor has been the existence of side friction along rural roads in India. Roadside markets are generally found at a frequent intervals along rural highways. Interactions among fast moving vehicles, pedestrians, and slow-moving local vehicles occur in a unique and random manner. The objective of the present work is to quantify the impact of roadside friction generated by roadside markets on travel speed, capacity, and level of service (LOS) of rural highways in India. A roadside friction index (RSFI) has been developed to quantify side friction. Using data collected from three study sections, speed-flow curves were developed for various side friction levels. The k-mean cluster analysis algorithm was applied for determining threshold values for LOS. Five threshold limits for LOS are recommended for a stable flow zone considering percentage speed reduction (PSR) and volume:capacity (
) ratio as measure of effectiveness. Impacts of side friction on the capacity of rural highway are also investigated.