Webinars from 2019
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.