In this paper, the use of the combined deterministic-stochastic subspace identification algorithm for the experimental modal analysis of mechanical structures is discussed. The algorithm requires artificial forces to be applied to the structure, so it can be used for experimental modal analysis (EMA). The algorithm can also be used for operational modal analysis (OMA), since the excitation level of the artificial force(s) can be low compared to the excitation level of the ambient forces. Both the modes that are artificially excited and those that are excited by the ambient forces are identified. This type of operational modal analysis is called an OMAX analysis (Operational Modal Analysis with eXogenous inputs) [1]. The main advantages of OMAX over OMA are that the modes that are excited by the artificial forces can be scaled to unity modal mass and that a higher number of modes can be identified.

Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural h...

https://journals.sagepub.com/doi/pdf/10.1177/14759217211036880

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights:

The construction industry has traditionally been characterised by the high diversity of its agents and processes, high resistance to change and low incorporation of technology compared to manufacturing industries However, the construction sector is experiencing now a strong renovation process in methodology and tools due to the incorporation of the Building Information Modelling, Lean Construction and Integrated Project Delivery Meanwhile, in production systems, “Industry 40” is a new paradigm that proposes automation, monitoring, sensorisation, robotisation, and digitalisation to improve production and distribution processes In this context, some authors have proposed the concept of “Construction 40” as the counterpart of Industry 40 for the construction sector, although the methodological-technological implications are not clear This research shows a methodological-technological framework adapted to the Architecture, Engineering, Construction, and Operations industry This papers includes a detailed proposal for a reference frameworks and related technologies that could impact on this sector, responding to its complexities and specific challenges, such as the unique spaces for each work, which are difficult to standardise, arbitrary cost overruns and a productivity far below the average for other industries, increasing competitiveness and globalisation, as opposed to its traditionally local deployment, and an increasing demand to reduce the carbon footprint for all its activities

Methodological-Technological Framework for Construction 4.0

Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Lost data recovery for structural health monitoring based on convolutional neural networks

Vibration signal denoising for structural health monitoring by residual convolutional neural networks

Data driven structural dynamic response reconstruction using segment based generative adversarial networks

This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques ar...

https://journals.sagepub.com/doi/pdf/10.1177/1475921720916881

Gao Fan

Papers

Lost data recovery for structural health monitoring based on convolutional neural networks

Vibration signal denoising for structural health monitoring by residual convolutional neural networks

Data driven structural dynamic response reconstruction using segment based generative adversarial networks

Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks

Improved automated operational modal identification of structures based on clustering