The stadium structures have unique structural features increasing the significance of structural monitoring systems specifically designed for them. Aside from vibrations serviceability concerns and human -induced excitations, the development and propagation of structural damage under all possible atmospheric and seismic conditions need to be closely monitored for structural resiliency and integrity of the stadia. As such, Structural Health Monitoring (SHM) methods combined with effective data evaluation methodologies need to be deployed to monitor the structural performance of stadiums. Even though stadia monitoring has been performed at multiple locations in the world, a web based and real-time SHM network of stadia is not known to authors. As a preliminary study for the network implementation of stadia monitoring with acceleration measurements, the presented work focuses on the fundamental steps to accomplish this goal, with a collaborative research effort between Qatar University, the University of Central Florida, and University of Alberta. The authors performed analytical investigations and experimental testing on stadium -type structures built in laboratory environments for the development of the SHM framework. Specialized signal processing algorithms, sensing suites and approaches considering multi -scale monitoring were used on collected acceleration measurements. The novelty of the work presented in this manuscript are the following items which exist simultaneously in the developed SHM framework. The developed framework is a web -based monitoring application where structural damage is detected in real-time. The proposed methodology operates directly on raw acceleration signals and runs at a network level. With that, the damage detection, damage localization, and damage quantification tasks are performed simultaneously, while the feature extraction and classification stages are combined in one learning body.

The strength of cross laminated timber (CLT) depends on the stiffness and strength of the lamellas and on thestrength of the finger joints. A model for how stiffness and strength vary along and between lamellas is used incombination with a finite element model of CLT and Monte Carlo simulations to calculate out-of-plane bendingstrength of homogeneous and inhomogeneous CLT. Calculated and experimentally obtained results of characteristicbending strengths, coefficient of variation of bending strength and the proportion of finger joint failures,agree very well for both types of CLT. The characteristic out-of-plane bending strength and the mean bendingstiffness were 23% and 16% higher, respectively, for inhomogeneous CLT with outer layer lamellas graded in thestrength class C35, compared to homogeneous CLT with all lamellas graded in the class C24. Simulation resultsgive basis for simple equations by which bending strength of CLT can be determined as function of the layup, thestrength class of outer layer lamellas and characteristic strength of the finger joints. Furthermore, system effectsare investigated. For inhomogeneous CLT, with outer layer lamellas of high strength class, the system effects turnout to be quite different from those of ordinary, homogeneous CLT.

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered, e.g., a different speed or load, or for different fault types/severities with sensors placed in different locations. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero -shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.

In softwood species, annual ring width correlates with various timber characteristics, including the density and modulus of elasticity along with bending and tensile strengths. Knowledge of annual ring profiles may contribute to more accurate machine strength grading of sawn timber. This paper proposes a fast and accurate method for automatic estimation of ring profiles along timber boards on the basis of optical scanning. The method utilizes two 1D convolutional neural networks to determine the pith location and detect the surface annual rings at multiple cross-sections along the scanned board. The automatically extracted rings and pith information can then be used to estimate the annual ring profile at each cross-section. The proposed method was validated on a large number of board cross-sections for which the pith locations and radial ring width profiles had been determined manually. The paper also investigates the potential of using the automatically estimated average ring width as an indicating property in machine strength grading of sawn timber. The results indicated that combining the automatically estimated ring width with other prediction variables can improve the accuracy of bending and tensile strength predictions, especially when the grading is based only on information extracted from optical and laser scanning data.(C) 2022 The Author(s). Published by Elsevier Ltd.

Wood is widely used in the construction sector and gaining increased market share. It isinteracting with the surrounding so that its mechanical and geometrical properties (stiffness,strength, swelling, density, …) change with temperature and humidity levels. In a full-scalebuilding, the eigenfrequencies are hence also varying with the climate. In the current paper,results from a preliminary experimental study are presented. A beam made from cross-laminated timber was hanging freely supported inside a climate chamber. Enforced vibrationsfrom a controlled shaker were taken to obtain the eigenfrequencies. With decreasing moisturecontent, the first and third eigenfrequencies were increasing (bending modes) while the secondeigenfrequency was decreasing (torsional mode). A finite element study allowed for checkingwhich parameters is influencing to which degree so that individual changes can be combined.

A computer-implemented method for generating a training dataset for training an artificial neural network configured to use images of lateral faces of a timber board to provide information about structure and/or defects, the method including; a log generation step during which a virtual model of a log is generated; a sawing step of the virtual model to obtain one or more virtual timber boards; a pattern step during which a surface pattern is determined as the intersection between the virtual lateral face and the internal structure and/or defects; a rendering step during which a rendered surface image of the lateral face of the virtual timber board is created; and an input data generation step during which the rendered surface images are used to create one or more item of input data; an output data generation step during which an item of output data is generated; and a population step during which a record is added to the training dataset comprising the item of input data, in combination with the item of output data.

A computer-implemented method for estimating a pith location with regard to a timber board, including: receiving a pixelated actual digital image of each lateral face of at least a longitudinal part of the timber board, extending along a longitudinal axis of the timber board; identifying an input portion in said longitudinal part of the timber board, where the input portion is a portion of the timber board which extends along the longitudinal axis; extracting from each pixelated actual digital image of the longitudinal part of the timber board, an input image representing said input portion, so obtaining four input images representing an appearance of the input portion at each lateral face of the timber board; inputting said four input images into the input layer of an artificial neural network and making the artificial neural network operate; and reading, at an output layer of the artificial neural network, output data defining a location of a pith of a log from which the timber board has been obtained, in a plane perpendicular to the longitudinal axis of the timber board at the input portion.

Timber-concrete hybrid buildings are an innovative solution to increase the amount of timber materials used in modern buildings. This study presents a dynamic evaluation of a nine-story timber-concrete hybrid residential building during construction. The building consists of a seven-story structure in cross-laminated timber (CLT) on top of two stories in concrete. Ambient vibration tests were conducted seven times during the 13-month construction period, including tests with only the structural elements in place and tests of the finished building with the façade, non-structural walls, and other internal finishing. The results show a clear decrease in the natural frequencies of the building as the building gets higher and more elements are installed. However, a slight increase in the natural frequency was observed following the installation of the non-structural walls in the final construction stage. A corresponding finite element analysis is presented for each test, providing additional insights into the parameters typically used in the structural design process. The study demonstrates the importance of properly selecting reduction factors for CLT elements in a dynamic finite element analysis. It also shows the importance of considering non-structural walls, both regarding weight and stiffness, even in buildings where the number of non-structural walls is relatively small compared to structural walls.

Optical scanning and X-ray computed tomography (CT) scanning of sawn timber provide a large numberof data points, on which data-driven numerical models can be based for simulations. These models require informationabout the deviations of the fibre orientations in the vicinity of knots. Optical scanning can be used to measure the in-planefibre orientation on wood surfaces. In CT scans of sawn timber, the fibre orientation around knots can be estimated usinga new fibre reconstruction algorithm based on the density gradient. The goal of this paper is to compare and synchroniseoptical and CT scanning data of sawn timber and then use the combined data set to evaluate fibre orientations derivedfrom both representations. The material comprised sawn timber of Norway spruce, in which alignment holes were drilled.The timber was scanned in an industrial CT scanner and subsequently in an industrial optical scanner where scanning wasrepeated after successive planing of the sawn timber surface. The results show that a projective mapping in combinationwith a spline interpolation are required for synchronisation, and that the in-plane fibre orientations calculated from thedensity gradients are qualitatively similar to the orientations derived from the optical data.

Korslimmat trä (KL-trä) tillverkas av brädor som limmas ihop korsvis i flera skikt. Skivorna som erhållsanvänds som byggelement, mestadels för väggar och bjälklagselement. Utgångsmaterialet trä tarstår i jämvikt med det omgivande klimatet och kommer därför ändra fuktkvoten. I studien undersöktesen skiva av KL-trä under varierande fuktförhållanden i en klimatkammare hos Linnéuniversitetet.Egenfrekvenser samt fuktkvoten följdes upp och sambandet med klimatet studerades. Det visade sigatt första och tredje uppmätta egenfrekvensen (böjning) visade negativ korrelation med omgivandefukten, den gick upp när fukten minskades (och tvärtom). För andra egenfrekvensen (torsion)däremot visade sig ett mer komplicerat samband. Ett flertal möjliga orsaker presenteras som förklaring. Medverkande organisationer var Linnéuniversitetet som huvudpart samt SödraSkogsägarna och Saab som bidragit som stödfunktionen och bollplank.