Optimal Sensor Placement in Reduced-Order Models Using Modal Constraint Conditions

Sensor measurements of civil structures provide basic information on their performance. However, it is impossible to install sensors at every location owing to the limited number of sensors available. Therefore, in this study, we propose an optimal sensor placement (OSP) algorithm while reducing the system order by using the constraint condition between the master and slave modes from the target modes. The existing OSP methods are modified in this study, and an OSP approach using a constrained dynamic equation is presented. The validity and comparison of the proposed methods are illustrated by utilizing a numerical example that predicts the OSPs of the truss structure. It is observed that the proposed methods lead to different sensor layouts depending on the algorithm criteria. Thus, it can be concluded that the OSP algorithm meets the measurement requirements for various methods, such as structural damage detection, system identification, and vibration control.

Solution of Navier-Stokes equations for fluids with magnetorheological compensation used in structures with energy dissipaters

The fixed-wall rectangular cavity flow problem is a classic problem that has been studied since the beginning of computational fluid mechanics. The present work aims to provide a numerical and computational solution of the Navier-Stokes equations using the finite difference method, applied to model the problem of a magnetorheological fluid in a rectangular cavity with a fixed wall in shock absorbers devices, used in civil structures that use energy dissipators.

Analysis of the mechanical behavior of adobe walls without reinforcement through computational modelling

The construction of civil structures on land has played an important role for centuries, however, due to the seismic requirements and the minimum safety standards that are currently required for any structure, this type of construction has been lagged, it is denoted that the related regulations they are widely dispersed and in most cases. In developed countries, numerous technical and legal problems arise to carry out construction with these materials. In relation to this work, a set of models of raw earth type walls are presented, through the SAP 2000 software, having as a supply of the mechanical properties of this material the Peruvian regulation E.080. For the analysis of these models, a static linear analysis for finite elements and a stress analysis of the service limit state concept were studied. Finally, the models with their respective stress studies, management and design recommendations are presented under the criteria of the analyses carried out, leaving open the possibility of both carrying out an experimental phase to develop the analogy with the postulates and proposed results, as well as such as the option to perform a static pressure analysis by finite elements in order to achieve greater precision and calibration of the model with respect to what can be evidenced in laboratory tests.

Automated band selection for Bayesian FFT modal identification Based on RetinaNet

Bayesian FFT algorithm is a popular method to identify modal parameters, e.g., modal frequencies, damping ratios, and mode shapes, of civil structures under operational conditions. It is efficient and provides the identification uncertainty in terms of posterior distribution. However, in utilizing the Bayesian FFT algorithm, it is tedious to manually select frequency bands and initial frequencies. This step requires professional knowledge and costs most of time, which prevents the automation of Bayesian FFT algorithm. Regarding the band selection as an object detection problem, we design a band selection network based on the RetinaNet to automatically select frequency bands and a peak prediction network to predict the initial frequencies. The designed networks are trained using the singular value spectrum of measured ambient vibration data and verified by various data sets. It can achieve the human accuracy with much less operation time, and thus provides a corner stone for the automation of Bayesian FFT algorithm.

A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks

In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost).

Distinct Advantages of Circumferential Notch Tensile (CNT) Testing in the Determination of a Threshold for Stress Corrosion Cracking (KISCC)

Stress corrosion cracking (SCC) is a vexing problem for load-bearing equipment operating in a corrosive environment in various industries, such as aerospace, chemical and mineral processing, civil structures, bioimplants, energy generation etc. For safe operation, effective maintenance and life prediction of such equipment, reliable design data on SCC (such as threshold stress intensity for SCC, i.e., KISCC) are invaluable. Generating reliable KISCC data invariably requires a large number of tests. Traditional techniques can be prohibitively expensive. This article reviews the determination of KISCC using the circumferential notch tensile (CNT) technique, the validation of the technique and its application to a few industrially relevant scenarios. The CNT technique is a relatively recent and considerably inexpensive approach for the determination of KISCC when compared to traditional techniques, viz., double-cantilever beam (DCB) and compact tension (CT) that may be fraught with prohibitive complexities. As established through this article, the CNT technique circumvents some critical limitations of the traditional techniques.