Structural damage detection using deep learning and FE model updating techniques.

The structural condition can be estimated by various methods. Damage detection, as one of those methods, deals with identifying changes in specific features within structural behavior based on numerical models. Since the method is based on simulation for various damage conditions, there are limitations in applicability due to inevitable discrepancies between the analytical model and the actual structure. Finite element model updating is a technique for establishing a finite element model that can reflect the current state of a target structure based on the measured responses. It is performed based on optimization for various structural parameters, but the final output can converge differently depending on the initial model and the characteristics of the algorithm. Although the updated model may not faithfully replicate the target structure as it is, it can be considered equivalent in terms of the relationship between the structural properties and behavioral characteristics of the target. This allows for the analysis of changes in the mechanical relationships established for the target structure. The change can be related to structural damage, and artificial intelligence technology can provide an alternative solution in such complex problems where analytical approaches are challenging. Taking practical aspects from the aforementioned methods, a novel structural damage detection methodology is presented in this study for identifying the location and extent of the damage. Model updating is used to establish a reference model that reflects the structural characteristics of the target. Training data for various damage conditions based on the reference model allows the artificial intelligence networks to identify damage to the target structure.

Effect of air bubbling on electroless Pd plating for the practical application of hydrogen selective membranes.

In this study, an air bubbling electroless plating (ELP) method was newly developed for the production of Pd composite membranes. The air bubble ELP alleviated the concentration polarization of Pd ions, making it possible to achieve a plating yield of 99.9% in 1 h and form very fine Pd grains with a uniform layer of ∼4.7 µm. A membrane with a diameter of 25.4 mm and a length of 450 mm was produced by the air bubbling ELP, achieving a hydrogen permeation flux of 4.0 × 10-1 mol m-2 s-1 and selectivity of ∼10 000 at 723 K with a pressure difference of 100 kPa. To confirm the reproducibility, six membranes were produced by the same method and assembled in a membrane reactor module to produce high-purity hydrogen by ammonia decomposition. Hydrogen permeation flux and selectivity of the six membranes at 723 K with a pressure difference of 100 kPa were 3.6 × 10-1 mol m-2 s-1 and ∼8900, respectively. An ammonia decomposition test with an ammonia feed rate of 12 000 mL min-1 showed that the membrane reactor produced hydrogen with >99.999% purity and a production rate of 1.01 Nm3 h-1 at 748 K with a retentate stream gauge pressure of 150 kPa and a permeation stream vacuum of -10 kPa. The ammonia decomposition tests confirmed that the newly developed air bubbling ELP method affords several advantages, such as rapid production, high ELP efficiency, reproducibility, and practical applicability.

Structural finite element model updating considering soil-structure interaction using ls-dyna in loop.

In this study, a finite element model updating method which can consider soil-structure interaction was developed to analyze the effect of soil properties on the structural response while considering interaction between the soil and the structure. Additionally, LS-DYNA, a commercial finite element program, was included in the loop of the proposed technique using MATLAB to conveniently utilize the complex structures updated by the model. To validate the performance of the proposed method, a large-scale shake table test was conducted. The objective of the validation test was to seek how accurately the proposed model updating method can detect the change in the stiffness. To compare the result of the proposed method with the conventional method, the model updating procedure was conducted with and without considering soil-structure interaction. The proposed finite element model updating method which considers the soil-structure interaction estimated the stiffness of the structure with maximum accuracy of 91%, while the conventional finite element model updating without considering the soil-structure interaction showed maximum accuracy of 88%. By comparing the proposed method with the conventional method without considering the soil-structure interaction, it was confirmed that the proposed method had an 3% higher accuracy on average.

Vision-Based Cable Displacement Measurement Using Side View Video.

Recent tragedies around the world have shown how accidents in the cable-stayed bridges can wreak havoc on the society. To ensure the safety of the cable-stayed bridges, several studies have estimated the cable tension force using the vibration of cables. Most of these methods for estimating the tension of a cable start with measuring the displacement of the cable. Recent development of commercial cameras provide opportunity for more convenient and efficient method for measuring the displacement of cable. However, traditional vision-based displacement measurement methods require the assumption that the movement of the cable should be measured in parallel to the camera plane. This assumption limits the installation location of the camera when measuring the displacement of a cable. Therefore, this study introduces a new vision-based cable displacement measurement system that can measure the displacement of a cable in various locations even when the camera is installed in the side of the cable. The proposed method consists of three phases: (1) camera projection matrix estimation, (2) cable tracking in the image coordinate, and (3) cable displacement estimation in the world coordinate. To validate the performance of the proposed method, a simulation-based validation test, a lab-scale validation test, and an on-site validation test were conducted. The simulation-based validation test verified the performance of the proposed method in an ideal condition, and the lab-scale validation test showed the performance of the method in physical environment. Finally, the on-site validation test showed that the proposed method can measure the cable displacement with a side view camera.

Non-Target Structural Displacement Measurement Using Reference Frame-Based Deepflow.

Displacement is crucial for structural health monitoring, although it is very challenging to measure under field conditions. Most existing displacement measurement methods are costly, labor-intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV)-based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target-based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.

A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices.

Due to the nature of real-world problems in civil engineering, students have had limited hands-on experiences in structural dynamics classes. To address this challenge, this paper aims to bring real-world problems in structural dynamics into classrooms through a new interactive learning tool that promotes physical interaction among students and enhances their engagement in classrooms. The main contribution is to develop and test a new interactive computing system that simulates structural dynamics by integrating a dynamic model of a structure with multimodal sensory data obtained from mobile devices. This framework involves integrating multiple physical components, estimating students' motions, applying these motions as inputs to a structural model for structural dynamics, and providing students with an interactive response to observe how a given structure behaves. The mobile devices will capture dynamic movements of the students in real-time and take them as inputs to the dynamic model of the structure, which will virtually simulate structural dynamics affected by moving players. Each component of synchronizing the dynamic analysis with motion sensing is tested through case studies. The experimental results promise the potential to enable complex theoretical knowledge in structural dynamics to be more approachable, leading to more in-depth learning and memorable educational experiences in classrooms.

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles.

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach.