Webinars from 2019
Since the 1970s, simulation training has developed operational trainers for a variety of complex systems from pilot flight simulations to cultural awareness training simulations. When connected to the real world, such simulation training interfaces can drive real vehicles and systems. We have been building a Simulation Training And Control System (STACS) for autonomous bridge inspection that uses a simulated world to train inspectors to control a heterogeneous group of robots. The objective is that, once trained, inspectors can use the same STACS interface used in training to control multiple real robots simultaneously during a bridge inspection task. We first built a multi-robot control interface and simulated environment so that a single operator may manage at least two types of robots. Second, we developed a new optimization algorithm for automatically and quickly generating near-optimal routing for n robots to cooperatively cover every truss while minimizing inspection time. This webinar describes and demonstrates STACS, provides optimization results corresponding to time (and thus cost) saved in bridge inspection. Results show that we can significantly reduce bridge inspection time with inspection robots and that the time needed decreases in inverse proportion to the number of robots available for inspection.
Across the country, bridge structures are aging with use commonly extending beyond their original design lives. Decisions to repair, retrofit or rehabilitate these structures will support continued reliability and resilience of these structures. To better understand the states of the bridges at any point in time, there are new technologies to inspect, monitor and assess bridge conditions.
To effectively use the results of these inspections and monitoring activities, we need an understanding of the relation between varying inspection parameters and the predicted performance of bridge structures.
In this seminar, we will describe methods to use inspection data to update assessment of bridge performance, focusing on corrosion inspection data. Through bridge model updating, we account for the effects of corrosion, including reduction of longitudinal and transverse reinforcement and bond deterioration between the steel and concrete through corrosion-induced cracking in reinforced concrete bridges. Corrosion is measured by percent mass loss. The impact of measured corrosion parameters on performance is assessed with results quantifying the increase in risk or vulnerability of these structures as corrosion levels increase. Comparing results across bridges supports risk-informed decisions in the management of bridges to protect these structures and ensure their reliability and resilience under future loading and hazard scenarios.
Performance Based Design for Bridge Piers Impacted by Heavy Trucks, Anil K. Agrawal
Based on bridge failure data compiled by New York State Department of Transportation, collision, both caused by vessel and vehicles, is the second leading cause of bridge failures after hydraulic. Current AASHTO-LRFD (2012) recommends designing a bridge pier vulnerable to vehicular impacts for an equivalent static force of 600 kips (2,670 kN) applied in a horizontal plane at a distance of 5.0 feet above the ground level. This research presents a performance-based approach for designing a bridge pier subject to impacts by tractor-semi-trailer weighing up to 80,000 lb based on an extensive investigation using finite element model of a tractor-semitrailer in LS-DYNA. In order to ensure the reliability of the proposed approach, parameters of concrete model were calibrated using small-scale impact test and were validated using a large scale test. Mechanics and modes of failure of bridge pier bents during vehicular impacts were verified through pendulum impact test on a large scale model of three column pier-bent system. A performance-based approach in terms of shear distortion, plastic rotation and demand / capacity (D/C) ratio has been proposed for the design of bridge piers vulnerable to heavy vehicle impacts.
A battery-free antenna sensor can wirelessly measure strain on a structure. Bonded to the surface of a base structure, the antenna sensor deforms when the structure is under strain, causing the antenna’s electromagnetic resonance frequency to change. This resonance frequency change can be wirelessly interrogated and recorded by a reader through electromagnetic backscattering. A radio frequency identification (RFID) chip on the sensor harnesses a small amount of energy from the interrogation signal and responds to the reader. The resonance frequency change identified by the reader is then used to determine the strain applied on the structure. The latest antenna sensor prototype adopts a thermally stable substrate as demonstrated in outdoor tests. Considering nonlinear constitutive relations, multi-physics simulation is performed to more accurately model the behaviors of the antenna sensor. In both simulation and laboratory experiments, the antenna sensor is shown to be capable of wirelessly measuring small strain changes. Finally, an emulated crack testing of the antenna sensor is presented, demonstrating the capability of measuring crack growth in application settings.
Artificial Intelligence, particularly deep learning, has recently received increasing attention in many applications, such as image classification, speech recognition, and computer games. The success of deep learning algorithms requires big annotated datasets for training, gradient-based optimization algorithms, and powerful computational resources. In the case of civil infrastructure inspection, we can collect big data from different imaging sensors such as color, thermal, and hyperspectral cameras. Three issues encounter in this application. First, it is tedious and expensive to let human experts annotate the datasets to train deep learning algorithms. Second, the offline trained deep learning algorithms may not be able to adapt to new civil infrastructures. Third and lastly, the trained deep learning algorithm works like a black box on new data, without the domain knowledge from human experts. In this project, we investigate intelligent data analytics algorithms with human experts in the loop, called Assistive Intelligence (AI). Using the bridge inspection as a case study, we aim to find regions-of-interest (e.g., joints with damages) over long video sequences. The data analytics algorithm is initially trained from a small set of data. Given the dataset of a new bridge, bridge experts only need to annotate a few region-of-interest examples as the seed; our algorithm will retrieve corresponding examples in the rest of videos. Human experts can also return some incorrectly retrieved samples to the data analytics algorithm for further refinement. Thus, while the data analytics algorithm can assist human in an efficient way, bridge experts can leverage their domain knowledge in the adaptation of the computational tool in different scenarios.
Webinars from 2018
In addition to visual inspection for surface flaws, inspectors are often required to detect subsurface defects (e.g., delamination and voids) using nondestructive evaluation (NDE) instruments, such as ground penetration radar (GPR) and impact sounding device, in order to determine the structural integrity of bridges and tunnels. In these cases, access to critical locations for reliable and safe inspections is a challenge.
Since 2002, Dr. Jizhong Xiao’s group has developed four generations of wall-climbing robots for NDE inspection of bridges and tunnels. These robots combine the advantages of aerodynamic attraction and suction to achieve a desirable balance of strong adhesion and high mobility. For example, Rise-Rover with two drive modules can carry up to 450 N payload, and GPR-Rover can carry a small GPR antenna for subsurface flaw detection and utility survey on concrete structures. These robots can reach difficult-to-access areas (e.g., the bottom side of bridge decks), take close-up pictures, record and transmit NDE data to a host computer for further analysis. They can potentially make bridge inspection faster, safer, and cheaper without affecting traffic flow on roadways.
This presentation will review the recent development of smart and autonomous wall-climbing robots to realize automated inspection of civil infrastructure with minimal human intervention.
Steel structures and steel bridges, constituting a major part in civil infrastructure, require adequate maintenance and health monitoring. In the U.S., more than 50,000 steel bridges are either deficient or functionally obsolete, which likely presents a growing threat to people's safety. The collapse of numerous bridges recorded over the past 16 years has shown significant impact on the safety of all travelers.
In this presentation, the design and implementation of two different climbing robots for steel structure inspection are reported. Based on the magnetic wheel design, the robot can climb on different steel surface structures (i.e., flat, cylinder, cube). The robots can be remotely controlled or programmed to move autonomously on steel structures. Current tests shows that the robots can carry up to 8 pounds of load while being able to adhere strongly on the steel surface. Climbing capability tests are done on bridges and on several steel structures with coated or unclean surfaces. Although the steel surface is curved and rusty, the robots can still adhere tightly.
The relatively small wavelengths and large bandwidths associated with microwave signals make them great candidates for inspection of construction materials and structures, and for materials characterization and imaging. Signals at these frequencies readily penetrate inside of dielectric materials and composites and interact with their materials characteristics and inner structures. Water molecule is dipolar and possesses a relatively large complex dielectric constant, which is also highly sensitive to the presence of ions that increase its electrical conductivity. Consequently, chemical and physical changes in construction materials affect their complex dielectric constant. This can be measured, and through analytical and empirical dielectric mixing formulae, correlated to those changes. Examples of applications would be, presence of delamination in a bridge deck and pavement, permeation of moisture behind retaining walls or corrosion of reinforcing steel bars which can be imaged with microwave techniques. One of the critical trade-off issues is between the microwave signal penetration into concrete vs. frequency of operation. Dielectric of concrete, particularly when moist, has a relatively high loss factor. As such, lower microwave frequencies are suitable to achieve reasonable penetration. Image resolution degrades as a function of decrease in operating frequency, therefore, a balance must be reached when using these techniques for imaging cement-based materials. In this webinar, issues related to concrete materials property evaluation and high-resolution imaging will be discussed, and examples will be provided.
Webinars from 2017
Unmanned aerial systems (UAS or “drones”) are a rapidly developing technology that can help meet the needs of transportation agencies for reliable, repeatable data that can save money and increase safety for the data collection process. By taking advantage of flexible platforms that can deploy a variety of sensors, transportation agencies and their information suppliers can help meet these data needs for operations, asset management, and other areas. Location-specific data on infrastructure condition and distresses can help with improved management of assets.
In this presentation, recent applied research led by a Michigan Technological University team is reviewed, with a focus on bridge condition assessment and corridor monitoring. Examples of 3D optical, thermal, and LiDAR data are shown and how analysis methods result in usable information to meet pressing data needs. Finding spalls and delaminations, characterizing cracking, inventory of roadway assets, and related applications will be shown. Achievable resolutions and accuracies will be reviewed and how these data are transformed into asset condition data.
There are over 600,000 bridges in the U.S. National Bridge Inventory (NBI). Nearly 50% of them rapidly approach their design life and deteriorate at an alarming rate, particularly under an increasing volume of overweight trucks. Visual inspection as the current practice in bridge management is labor intensive and subjective, resulting in inconsistent and less reliable element ratings. Lab-on-sensor technologies can provide supplemental mission-critical data to the visual inspection for both qualitative and quantitative evaluations of structural conditions, and thus critical decision-making of cost-effective strategies in bridge preservation.
In this presentation, the design and operation characteristics of highway bridges are first reviewed to establish the needs for structural behavior monitoring in order to align monitoring outcomes with daily practices in bridge preservation. Next, a lab-on-sensor design theory is presented and applied to detect and assess structural behaviors such as concrete cracking, foundation scour, and steel corrosion. Finally, the accuracy, resolution and measurement range of various sensors are discussed before this presentation is concluded.