Automatic Estimation of Natural Frequencies and Damping Ratios of Building Structures

Mimura, Takuya; Mita, Akira

doi:10.1016/j.proeng.2017.04.470

Cited by 8 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, Mimura and Mita [ 185 ] proposed an automatic evaluation method for obtaining frequencies and damping ratios under the assumption that the information on mass distribution and pattern shapes was available. They tested 40-story steel structural models built with RC.…”

Section: Trending Applicationsmentioning

confidence: 99%

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

Al-hababi

Cao

Saleh

et al. 2020

Sensors

View full text Add to dashboard Cite

In recent decades, nonlinear damping identification (NDI) in structural dynamics has attracted wide research interests and intensive studies. Different NDI strategies, from conventional to more advanced, have been developed for a variety of structural types. With apparent advantages over classical linear methods, these strategies are able to quantify the nonlinear damping characteristics, providing powerful tools for the analysis and design of complex engineering structures. Since the current trend in many applications tends to more advanced and sophisticated applications, it is of great necessity to work on developing these methods to keep pace with this progress. Moreover, NDI can provide an effective and promising tool for structural damage detection purposes, where the changes in the dynamic features of structures can be correlated with damage levels. This review paper provides an overview of NDI methods by explaining the fundamental challenges and potentials of these methods based on the available literature. Furthermore, this research offers a comprehensive survey of different applications and future research trends of NDI. For potential development and application work for nonlinear damping methods, the anticipated results and recommendations of the current paper can assist researchers and developers worldwide to find out the gaps and unsolved issues in the field of NDI.

show abstract

Section: Trending Applicationsmentioning

confidence: 99%

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

Al-hababi

Cao

Saleh

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…This enables facility managers to address current operational requirements, while also improving their real estate portfolio renewal forecasting and addressing funding for capital projects [11]. Current asset condition assessment procedures are extensively time consuming, laborious and expensive, and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access for inspection [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Perez

Tah

Mosavi

2019

Sensors

183

View full text Add to dashboard Cite

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

show abstract

“…In addition, methods that identify vibration characteristics, such as natural frequency and mode shape, and related physical parameters, such as story stiffness and damping, have been studied. [3][4][5] Changes in the values of these parameters are used to estimate the damage to structures. However, in these methods, although structural damage can be simply estimated using a small number of accelerometers, the relationship between these features and the damage is empirical, and the information does not reveal the location of the damaged members.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep neural network for detecting earthquake damage to brace members installed in a steel frame

Yamashita

Kohiyama

Watanabe

2020

Japan Architectural Review

View full text Add to dashboard Cite

We are developing an artificial intelligence system for structural health monitoring that can detect local damage in a building structure by using the E‐Simulator numerical simulation system that is being developed by the Japanese National Research Institute for Earth Science and Disaster Resilience. In this study, we confirmed the applicability of a multiclass classifier using a deep neural network to address the problem of identifying damage patterns in braces installed in a steel frame. Experimental data obtained from shaking table tests were used for training and testing. Cross‐validation tests were conducted for several cases with different numbers of sensors, sensor degrees of freedom, and nodes in the hidden layers of the network. The results demonstrated that the accuracy of the damage pattern detection from the constructed classifier exceeded 77% when the appropriate hidden layers were selected and reached 87.9% for the best case.

show abstract

Automatic Estimation of Natural Frequencies and Damping Ratios of Building Structures

Cited by 8 publications

References 3 publications

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

A Critical Review of Nonlinear Damping Identification in Structural Dynamics: Methods, Applications, and Challenges

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Deep neural network for detecting earthquake damage to brace members installed in a steel frame

Contact Info

Product

Resources

About