This paper summarizes the test recommendations for in situ assessment of structural timber using stress wave measurements as developed by members of the RILEM Technical Committee AST 215 "In-situ assessment of structural timber". In the first part, the basic principles, the equipment, and the practical application of stress-wave-based testing using the time-of-flight method are described. A detailed testing procedure provides hands-on information on the execution of in-field stress wave testing. A typical example is given to demonstrate step-by-step on how to evaluate stress wave readings and the health state of the inspected timber member. The latter part of the paper gives a short overview of the use of acoustic tomography and ultrasonic echo methods

This paper presents a non‐destructive, global, vibration‐based damage identification method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to identify defects. To extract damage features and to obtain suitable input parameters for ANNs, principal component analysis (PCA) techniques are applied. Residual FRFs, which are the differences in the FRF data from the intact and the damaged structure, are compressed to a few principal components and fed to ANNs to estimate the locations and severities of structural damage. A hierarchy of neural network ensembles is created to take advantage of individual information from sensor signals. To simulate field‐testing conditions, white Gaussian noise is added to the numerical data and a noise sensitivity study is conducted to investigate the robustness of the developed damage detection technique to noise. Both numerical and experimental results of simply supported steel beam structures have been used to demonstrate effectiveness and reliability of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.

This article presents a response-only structural health monitoring technique that utilises cepstrum analysis and artificial neural networks for the identification of damage in civil engineering structures. The method begins by applying cepstrum-based operational modal analysis, which separates source and transmission path effects to determine the structure's frequency response functions from response measurements only. Principal component analysis is applied to the obtained frequency response functions to reduce the data size, and structural damage is then detected using a twostage ensemble of artificial neural networks. The proposed method is verified both experimentally and numerically using a laboratory two-storey framed structure and a finite element representation, both subjected to a single excitation. The laboratory structure is tested on a large-scale shake table generating ambient loading of Gaussian distribution. In the numerical investigation, the same input is applied to the finite model, but the obtained responses are polluted with different levels of white Gaussian noise to better replicate real-life conditions. The damage is simulated in the experimental and numerical investigations by changing the condition of individual joint elements from fixed to pinned. In total, four single joint changes are investigated. The results of the investigation show that the proposed method is effective in identifying joint damage in a multi-storey structure based on response-only measurements in the presence of a single input. Because the technique does not require a precise knowledge of the excitation, it has the potential for use in online structural health monitoring. Recommendations are given as to how the method could be applied to the more general multiple-input case.

This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

This paper presents a comparative study using static and ultrasonic testing for the determination of the full set of orthotropic material properties of wood. In the literature, material properties are typically only available in the longitudinal direction, and most international standards do not provide details on the testing of the other two secondary directions (radial and tangential). This work provides a comprehensive study and discussions on the determination of all twelve orthotropic material properties of two hardwood species using static testing and an alternative testing approach based on ultrasonic waves. Recommendations are given on the execution of the tests and the interpretation and calibration of the results.