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Transportation Asset Management
On the design of optimal auctions for road concessions: Firm selection, government payments, toll and capacity schedules with imperfect information
Year: 2021
We consider firms with privately-known production efficiencies, captured in their cost structure, bidding for a road concession agreement with a government seeking to maximize the expected public welfare generated by the project. The setting is motivated by the increasing trend in road privatization around the world, and the need to design auctions leading to efficient outcomes: firm selection, government payments, toll and capacity schedules, which determine public welfare and firm profits. In this paper, we characterize optimal direct revelation mechanisms for cases with and without restrictions on government payments. Because, in practice, it may be difficult or unappealing for firms to reveal their cost structure, we derive a scoring function that allows for the implementation of the optimal mechanism as a first-score auction where bids consist of toll, capacity, and government payment levels. The function accounts for distortions stemming from firms’ incentives to exploit their private information. We use the results to benchmark the performance of simple, but sub-optimal mechanisms: (i) predetermined toll and capacity bidding, (ii) auctions where the public welfare function is used to score bids, and (iii) a demand pricing mechanism aimed at maximizing patronage.
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Comparative Study of Predictive Analysis Methods to Estimate Bridge Response
Year: 2019
Monitoring bridge performance is crucial to ensure safety and allocate resources in a cost-effective manner. This paper aims to reduce the gap between researchers and practitioners by showing how predictive analytics can be employed in the process of distilling operational information out of bridge monitoring data. Furthermore, it has the goal to aid infrastructure owners and managers in evaluating bridge performance over time and making data-driven decisions to prolong the life of the structure. To achieve this goal, the paper presents a comparative study of three predictive analysis models to estimate bridge response to heavy trucks: multilinear regression, artificial neural network, and regression tree. Following this comparison, an alternative strategy, based on the analysis of influential observations, is proposed. This approach brings together predictive power with other important capabilities such as explanatory capabilities and interpretability. The test bed structure is a short-span highway bridge which was monitored for 3 years using weigh-in-motion (traffic data) and structural health monitoring (bridge data) systems.
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Leveraging analytics to monitor and manage urban infrastructure
Year: 2018
We use structural time series models to predict the progression of measurements collected from infrastructure facilities, and statistical process control for model diagnosis/refinement, and to support detection of extraordinary events. The approach contributes tools to interpret the effect of unusual events that exploits the structural times series analysis paradigm, involving decomposition of measurements into unobserved, but meaningful components. As an example, we consider displacement measurements from a highway bridge. The model yields an estimate of the rate at which the bridge's position is drifting, which is useful to plan corrective measures. The model also provides a means to control for environmental factors affecting the accuracy of the measurements. In terms of unusual measurements, large measurement errors appear driven by extraordinary temperature variation, and the interaction of environmental factors with unusually large traffic or wind loads results in movement deviating significantly from the model's predictions.
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Dynamic learning process for selecting storm protection investments
Year: 2016
Increasingly aggressive weather events, such as hurricane-driven storm surges, threaten surface transportation systems and motivate defensive actions, including hardening. Decisions about the design and scale of hardening investments are informed by meteorological records. Historically based probabilities of severe storms are used in practice to define expected values of the intensity of weather assaults (e.g., the 100-year storm) and then to select defenses. The prospects of climate change and rising sea level suggest that assuming weather events are stationary may present added risks to surface transportation infrastructure, particularly in coastal environments. This paper proposes a dynamic, learningbased investment strategy, similar to the concept of real options, that updates estimates of storm surges on the basis of experience and recommends incremental hardening investments when observed trends indicate that additional defense is warranted. Monte Carlo simulation is used to compare and evaluate static (expected value-based) and dynamic investment strategies in the context of storm intensity patterns that are (a) known, (b) incorrectly estimated, and (c) nonstationary, with growing intensity. Results suggest that when the future is well described by past experience, the static, once-and-done decision strategy works well, but when the underlying storm generation process is unknown, or when it is changing (growing) in intensity, the learning-based dynamic strategy is especially advantageous. These results underscore the importance of flexibility in designing storm protection, of tracking weather events closely to detect emerging trends, and of data-driven decision strategies. This dynamic approach to decision making under uncertainty can be applied to other sources of uncertainty, for example, demand estimates.
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Intelligent Structural Health Monitoring of Vehicular Bridges
Researcher(s): Sridhar Krishnaswamy, Oluwaseyi Balogun
Year: 2010
After the catastrophic I-35W bridge collapse engineers have been seeking and pursuing alternative inspection techniques to monitor a bridge’s structural health. One technology that has been proven successful for such a purpose is acoustic emissions (AE). Current AE inspection systems use piezoelectric sensors to “listen” to the structure. These sensors are costly as each of them requires a preamplifier. During inspections, it is not uncommon for inspectors to pick up local radio stations with these sensors as they are susceptible to electromagnetic interference. The sensors also have a limited operating frequency range so most AE technicians carry around a box of transducers to cover the full 30-1000 kHz wave band. Piezoelectric sensors sensitivity will also decrease over time requiring a calibration prior to each use.
The proposed AE system utilizes optical Fiber Bragg Gratings (FBG) in place of piezoelectric sensors. FBG sensors are low cost and readily available, light-weight, immune to electromagnetic noise sources, and do not require preamplification. It is possible to set up a FBG array at great distances from the control box without any signal loss because the FBG are connected to the control box by fiber optic and not a cable. Having a significantly small foorprint than the piezoelectric sensors, the FBG can also be mounted in areas with small tolerances. Since they are small, the FBG can be installed permanently to the structure and the fiber that runs to each sensor can be concealed easily. This will allow for the inspector to leave the system in place to do SHM of the structure or to leave the sensors in place and simply hook up the demodulator box when a scheduled inspection is required. With this system inspectors are not required to set up, apply couplant, and recalibrate the piezoelectric sensors prior to each inspection. Finally, unlike piezoelectric sensors, FBG do not exhibit a reduction in sensitivity over time.
An Input-Output Approach for the Efficient Design of Sustainable Goods and Services
Researcher(s): Elaine Croft McKenzie, Durango-Cohen, Pablo Luis
Year: 2010
Purpose: We propose a prescriptive framework to support environmentally conscious decision making in the design of goods and services. The framework bridges recent applications of input-output analysis to conduct environmental life cycle assessment (LCA), with seminal work in production economics. In the latter, product design, production planning, and scheduling problems are frequently formulated as input-output models with substitution, and subsequently analyzed and solved as linear programs. The use of linear programming provides an appealing theory and computational framework to support decision making, as well as to conduct sensitivity analysis.
Methods: In this paper, we explore the benefits of integrating LCA within a linear programming (LP) framework and present a case study where we consider a hypothetical advertiser located in the Chicago Metropolitan Area, who wishes to allocate a predetermined budget to place ads in either the print or online versions of a high-circulation, local newspaper. We formulate the problem of finding an advertising strategy that minimizes global warming potential (GWP), subject to demand and budget constraints. We then solve the problem and evaluate the optimal strategy in terms of discharges of component greenhouse gases, and in terms of requirements imposed on various energy sources. We also analyze the sensitivity of the optimal advertising strategy (and associated global warming potential) to perturbations in the model parameters and constraints.
Results and Discussion: By embedding LCA within an LP formulation, we are able to examine the relationships between economic and environmental factors inherent within decisions to use specific products or services. Specifically, the advertiser finds that each strategy contains tradeoffs among and between environmental and monetary costs. A disaggregate comparison of greenhouse gas release and energy consumption among strategies highlights the variation between these factors and the potential dangers of aggregation. Sensitivity analysis gives us marginal costs (per dollar and per person) of GWP in the optimal solution. These and other managerial insights presented highlight the complex tradeoffs necessary for environmentally conscious, sustainable decision making.
Maintenance Optimization for Transportation Systems with Demand Responsiveness
Researcher(s): Pablo L. Durango-Cohen, Pattharin Sarutipand
Year: 2009
We present a quadratic programming framework to address the problem of finding optimal maintenance policies for multifacility transportation systems. The proposed model provides a computationally-appealing framework to support decision-making, while accounting for functional interdependencies that link the facilities that comprise these systems. In particular, the formulation explicitly captures the bidirectional relationship between demand and deterioration. That is, the state of the facility, i.e., its condition or capacity, impacts the demand/traffic; while simultaneously, demand determines a facility’s deterioration rate. The elements that comprise transportation systems are linked because the state of a facility can impact demand at other facilities. We provide a series of numerical examples to illustrate the advantages of the proposed framework. Specifically, we analyze simple network topologies and traffic patterns where it is optimal to coordinate (synchronize or alternate) interventions for clusters of facilities in transportation systems.
PIRE: US-Asia Network of Centers for Intelligent Structural Health Management of Safety-Critical Structures
Researcher(s): Jan Achenbach
Year: 2009
This project will join U.S., Indian, Chinese and South Korean partners in an international research and education project to establish a US-Asian network of centers for basic research on Intelligent Structural Health Management of safety-critical aerospace, mechanical, and civil structures. The aim is to develop ISHM systems that are applicable across disciplines and in diverse global settings. Sridhar Krishnaswamy, Professor of Mechanical Engineering and Director of the Center for Quality Engineering & Failure Prevention at Northwestern University leads a partnership with the University of Illinois, Chicago; Harbin Institute of Technology, China; Indian Institute of Technology, Madras, India; National Aerospace Labs, India; Pusan National University, South Korea; GE India Technology Center; Goodrich Corporation; Honeywell; and Boeing. On the U.S. side, this five-year project will train 22 graduate students, and 10 undergraduate students. This PIRE will implement an extensive exchange program of faculty and student research to develop novel sensor systems for on-line diagnostics, develop advanced instrumentation for off-line non-destructive characterization, investigate material degradation mechanisms to obtain damage growth laws for relevant material systems, and develop probabilistic methods for prognosis of the remaining lifetime of a structure.
Super-Tough Steel for Bridges and Other Applications
Researcher(s): S. Vaynman, M.E. Fine, Y-W Chung
Year: 2009
Higher toughness in steel permits the presence of longer fatigue cracks prior to rupture, making it less likely that they would escape detection. Since steel becomes brittle at cryogenic temperatures, steel with higher fracture toughness at low temperatures than existing steel is of interest for infrastructure applications such as bridges and tank cars. A steel (ASTM A710 Grade B) was developed at Northwestern University with FHWA and ITI financial support and with IDOT participation for bridge and other infrastructure applications This steel has a minimum of 70-ksi-yield strength significantly outperform previously used ASTM A36 and ASTM A588 bridge steels in strength, weldability and fracture toughness at low temperatures. There is interest in a steel with even much higher than for A710 Grade B steel fracture toughness at cryogenic temperatures for use by Northern states DOTs and also for tank cars that transport cooled liquids such as liquid chlorine.
We propose to develop and test a cryogenic super-tough steel by composition modification of our previously developed A710 Grade B steel. Based on our prior steel development experience we will select 2-3 compositions, order heats from CANMET and in collaboration with IDOT and Union Tank Car Company (UTLX) will thoroughly
investigate the mechanical, fracture, microstructural, welding and corrosion properties of steels. We will select the optimal composition for commercial implementation. We will contact Northern states DOTs and steel mills to market the steel for infrastructure as well as for other applications. We expect that at least one steel company will produce a small commercial heat and evaluate the mechanical properties. During the performance of the project we will prepare documentation for inclusion of the steel into appropriate standard.
We expect the cryogenic super-tough steel to respond to a need for a more fracture resistant steel for infrastructure, tank car, Homeland Security and other fracture critical applications.
Life Cycle Management of Steel Bridges Based on NDE and Failure Analysis
Researcher(s): Brian Moran, Jan Achenbach
Year: 2008
The finite element method is used to investigate failure mechanisms in pin-hanger connection in aging highway bridges. Bridge pins and hangers are typically considered as critical elements whose failure may result in partial or entire collapse of the structure. The primary function of a pin-hanger connection is to allow for longitudinal thermal expansion and thermal contraction in the bridge super-structure due to temperature changes (daily or seasonal). The induced movements, due to thermal effects, have considerable impact on bridge design and performance. Thus, in addition to the applied mechanical loads (dead load and traffic), the thermal load due to temperature changes is also included. A key goal was also to relate original design calculations (before the bridge was built) to the current analysis which accounts for the entire bridge structure under combined loads and extreme environmental conditions. Many bridges in use today have deteriorated due to aging, misuse, or lack of proper maintenance. After years of exposure to atmospheric environments (deicing salts and load variations), corrosion and wear tends to produce at least a partially fixed (or locked up) condition. Pack-rust (or corrosion buildup) can have two detrimental effects on the pin:
- First, the cross-section of the pin can “decrease” because of corrosive section loss. The corrosion can produce pitting that may act as crack initiation sites.
- Second, pack-rust can effectively “lock” the pin within the connection, so that no rotation is permitted. This may produce a likely location for the development and propagation of cracks.
Furthermore, bridge structure is nonlinear especially when the pin is in the locked condition and an elastic-plastic analysis is required to model the bridge behavior when the pin is locked. One of the main focuses in this study is on the determination of the three-dimensional (3D) crack growth in the pin, since the lifetime of the entire structure is dependent on the behavior of cracks. Due to the accessibility of 3D finite element programs and the comparatively low cost of computing time, it is state of the art to perform 3D analyses of complex engineering problems. The finite element program ABAQUS has been used throughout the investigation, which essentially includes:
- stress analysis
- thermal effects
- elastic-plastic analysis
- determination of the mixed-mode stress intensity factors (KI , KII , and KIII)
- fatigue crack growth simulation
Furthermore, since analytical solutions are not available in many cases, especially for this 3D problem (with complex geometry and loading conditions), a series of validation tests were performed on bridge components.
Iron-Based Foams
Researcher(s): David Dunand
Year: 2008
Due to their low cost and attractive thermo-mechanical properties, high-chromium ferritic steels are of particular interest in applications where good resistance to oxidation, high temperatures and high stresses are needed (i.e., filters or catalyst substrates). Additionally, they show promise in supplanting existing metallic and ceramic (non-porous) interconnects in solid-oxide fuel cells.
Justin is examining methods to create high-chromium, ferritic steel foams with an open-cell structure. Both microstructure and mechanical properties will be studied to optimize processing techniques.
Techniques for processing NiTi-based foams are being adapted to fit the needs of iron-based foams. Subsequently, microarchitecture and compressive mechanical properties will be examined to optimize foaming methods.
Intelligent Structural Health Management of Civil Infrastructure
Researcher(s): Sridhar Krishnaswamy
Year: 2008
The objective of this research project is to establish a global partnership of universities, laboratories, and companies to engage in cutting-edge research and education in the area of Intelligent Structural Health Management of safety-critical aerospace, civil & mechanical structures. Study partners for this project include: Northwestern University, Northwestern University Infrastructure Technology Institute, University of Illinois –Chicago, Harbin Institute of Technology, China, Indian Institute of Technology, Madras, India, Pusan National University, Korea, National Aerospace Labs, India, GE India Technology Center, Bangalore, Honeywell, Goodrich, and Boeing. Intelligent Structural Health Management systems incorporate diagnostic sensor data to make closed-loop prognosis of remaining structural integrity, thereby facilitating timely remedial actions to prevent catastrophic structural failure.
The goal of the PIRE-ISHM project is to develop globally-engaged scientific/technological leaders with a unique set of cross-disciplinary skills that is marketable across the globe and across disciplines. The program includes several unique features: (1) Cross-disciplinary curriculum; (2) International team projects; (3) Extended training and research visits to China, India and Korea for graduate students and research fellows; (4) Coordinated MS/PhD programs with global partners; (5) Summer internships for undergraduates; (6) Summer schools; (7) Language and culture training; (8) Exposure to issues relating to innovation and invention in a global context; and (9) Industrial internships for graduate students at global research centers of major US-based multinational companies prior to or after graduation. Research topics include: (1) Sensor Technology, (2) Smart Structures; (3) Multifunctional Materials; (4) Materials Science; (5) Nondestructive Characterization; (6) Structural Analysis; (7) Failure and Damage Models for Materials; (8) Probabilistic Prognosis of Remaining Lifetime; and (9) Decision-making
Estimation of Dynamic Performance Models for Transportation Infrastructure Using Panels Data
Researcher(s): C. Chu, P. Durango-Cohen
Year: 2008
We present state-space specifications of time series models as a framework to formulate dynamic performance models for transportation facilities, and to estimate them using panel data sets. The framework provides a flexible and rigorous approach to simultaneously capture the effect of serial dependence and of exogenous factors, while controlling for individual heterogeneity when pooling data across the facilities that comprise the panel. Because the information contained in time series and cross-section data are combined in the estimation, the ensuing performance models capture effects that are not identifiable in either pure time series or pure cross-section data. Also, pooling data across facilities leads to improved estimation results. To illustrate the methodology, we consider three classes of models for a panel of asphalt pavements from the AASHO Road Test. The models differ in the assumptions regarding the structure of the underlying mechanisms generating the data sequences. The results indicate that serial dependence is indeed significant, thereby reinforcing the importance of dynamic modeling. We also compare the specifications to assess the pool ability of pavement condition data. The results provide evidence that heterogeneity among the facilities is present in the panel. Finally, we highlight features that elude existing performance models developed with static modeling approaches: the ability to estimate maintenance activities as exogenous variables, and the capability of updating forecasts in response to inspections.
Creep of Dispersion-Strengthened Light Alloys
Researcher(s): David Dunand
Year: 2008
There are many practical examples where materials are required to survive for long periods under load at high temperatures - a classic example is in the turbine blades of jet engines. In the interest of improved efficiency and performance it is desirable to maximize the operating temperatures while minimizing overall weight. An aluminum alloy with high temperature capability, therefore, is an attractive alternative to the titanium-based components currently used in the frontal sections of jet engines.
Based on the behavior of nickel-base superalloys, which resist degradation of mechanical properties to approximately 75% of their absolute melting temperature, it is conceivable that aluminum-based alloys could be similarly developed which would be useful to 400 °C. As is true for γ’ in the nickel-base systems, a high-temperature aluminum alloy must contain a large volume fraction of a suitable dispersed phase, which must be thermodynamically stable at the intended service temperature.
The creep resistance of nickel-base superalloys is achieved by the presence of ordered Ni3(Al,Ti) precipitates. These precipitates, usually termed γ’, have the cubic L12 structure and are therefore isomorphous with the fcc Ni-alloy matrix (called the γ phase). The low mismatch in the lattice parameter between the γ matrix and the γ’ precipitates confer particle stabilities well beyond the levels possible with precipitates having a high particle/matrix interfacial energy, and hence these stable precipitates are effective barriers to dislocations at elevated temperatures.
An effective high temperature aluminum alloy should exhibit a similar structural constitution. Trialuminide intermetallic compounds (Al3X) have many attractive characteristics, such as low density, high specific strength, good heat resistance and excellent oxidation resistance. Therefore, they are excellent candidates for use as dispersoids or precipitates in the design of high strength Al alloys for high temperature applications. We investigate microstructure and creep properties of binary and ternary Al-Sc-X, Al-Ti-X and Al-Zr-X alloys with nanoscale, coherent, coarsening-resistant precipitates. Additions of submicron alumina dispersoids are also investigated.
Estimation of Infrastructure Performance Models Using State-Space Specifications of Time Series
Researcher(s): C. Chu, P. Durango-Cohen
Year: 2007
We consider state-space specifications of autoregressive moving average models (ARMA) and structural time series models as a framework to formulate and estimate inspection and deterioration models for transportation infrastructure facilities. The framework provides a rigorous approach to exploit the abundance and breadth of condition data generated by advanced inspection technologies. From a managerial perspective, the framework is attractive because the ensuing models can be used to forecast infrastructure condition in a manner that is useful to support maintenance and repair optimization, and thus they constitute an alternative to Markovian transition probabilities. To illustrate the methodology, we develop performance models for asphalt pavements. Pressure and deflection measurements generated by pressure sensors and a falling weight deflectometer, respectively, are represented as manifestations of the pavement’s elasticity/load-bearing capacity. The numerical results highlight the advantages of the two classes of models; that is, ARMA models have superior data-fitting capabilities, while structural time series models are parsimonious and provide a framework to identify components, such as trend, seasonality and random errors. We use the numerical examples to show how the framework can accommodate missing values, and also to discuss how the results can be used to evaluate and select between inspection technologies.
A Time Series Framework for Infrastructure Management
Funder: The Midwest Regional University Transportation Center
Researcher(s): Pablo Durango-Cohen
Year: 2007
We present an integrated framework to address performance prediction and maintenance optimization for transportation infrastructure facilities. The framework is based on formulating the underlying resource allocation problem as discrete- time, stochastic optimal control problem with linear dynamics and a quadratic criterion. Facility deterioration is represented as a time series which provides an attractive and rigorous approach to specify and estimate performance models. The state and decision variables in the framework are continuous which allows the framework to overcome important computational and statistical limitations that do not allow existing optimization models to address various problems that arise the management of transportation infrastructure. To illustrate the advantages of the proposed approach, we conduct a numerical study where we examine the case of multiple technologies being used simultaneously to collect condition data. Specifically, we illustrate how the framework can be used to quantify the effect of the capabilities of inspection technologies, i.e., precision, accuracy and relationships, on life-cycle costs. This information can be used to compute the operational value of combining technologies, and thus, to guide in their selection based on economic criteria.
Transformation Superplasticity of Metals, Composites and Intermetallics
Researcher(s): David Dunand
Year: 2005
Internal-stress plasticity is a mechanism used to increase the deformation rate of metals and alloys deforming by creep or low-temperature plasticity. When mismatch stresses or strains externally, they can be biased in the direction of an external stress, resulting in a strain increment in the same direction as the biasing stress, and with a magnitude proportional to the biasing stress. If the internal mismatch is constantly regenerated (usually through thermal cycling), this leads to an average strain-rate proportional to the applied stress, with a average strain rate sensitivity of unity which results in tensile strains well in excess of 100%, a phenomenon called internal-stress superplasticity.
One common method to produce repeatable internal stresses is to cycle the temperature around a phase transformation temperature, where the two coexisting allotropic phases have different densities. Transformation mismatch plasticity or transformation superplasticity (TSP) by thermal cycling has been observed in many allotropic metals and alloys and is particularly well-studied in Ti subjected to thermal α-β cycling. Recently, Zwigl and Dunand showed that chemical cycling at constant temperature could also produce transformation superplasticity: due to the very high diffusivity of hydrogen, α-Ti can be rapidly alloyed with hydrogen by exposure to a hydrogen-bearing atmosphere, which leads to the formation of the β-Ti phase; upon exposure to vacuum or a hydrogen-free atmosphere, the hydrogen diffuses out of the titanium, which results in a transformation back to the α-Ti phase.
In current work, we investigate hydrogen-induced TSP in titanium with the goal of experimentally separating phase transformation mismatch from lattice swelling mismatch. We explore the underlying deformation mechanisms and their dependencies on the processing parameters (hydrogen partial pressure, half cycle time and applied stress). Additionally, we investigate transformation superplasticity in other materials (Zr, Nb) and different geometries (wire vs. bulk).
A Simulation-Based Design Paradigm for Complex Cast Components Integrating Damange Tolerance Assessment, Casting Process and Non-destructive Evaluation Simulation
Researcher(s): Stephane P.A. Bordas, James G. Conley, Brian Moran, Joe Gray, Ed Nichols
Year: 2005
This paper describes and exercises a new design paradigm for cast components. The methodology integrates foundry process simulation, non-destructive evaluation (NDE), stress analysis and damage tolerance simulations into the design process. The foundry process simulation is used to predict an array of porosity related anomalies. The probability of detection of these anomalies is investigated with a radiographic inspection simulation tool (XRSIM). The likelihood that the predicted array of anomalies will lead to a failure is determined by a fatigue crack growth simulation based on the extended finite element method, XFEM and therefore does not require meshing nor remeshing as the cracks grow. In this approach, the casting modelling provides initial anomaly information, the stress analysis provides a value for the critical size of an anomaly and the NDE assessment provides a detectability measure. The combination of these tools allows for accept/reject criteria to be determined at the early design stage and enables damage tolerant design philosophies. The methodology is applied to the design of a cast monolithic door used on the Boeing 757 aircraft.