Presence of knots and associated fiber deviation are crucial for engineering properties of sawn timber. Yet, there is a notable absence of a thoroughly calibrated and verified mathematical model for fiber directions. This gap is largely due to the lack of comprehensive and detailed experimental data on growth surface geometry and 3D fiber orientation. Such data, ideally extracted at the sawn timber level, should include diverse information related to single knots, multiple knots, knot clusters, and both live and dead knots. This study presents a comprehensive laboratory examination of a full-size Norway spruce timber board. The extraction of knots, growth surfaces, and full-volume 3D fiber directions was successfully achieved, yielding highly detailed experimental data. The method developed comprises X-ray computed tomography for 3D knot and growth surface geometry, and optical scanning utilizing the tracheid effect for in-plane fiber directions. A limitation was identified when the normal vector of growth surfaces and the normal vector of the optically scanned board surface are orthogonal but a sensitivity analysis revealed that an angle error introduced to the in-plane fiber directions has limited impact on the computed 3D fiber vectors when the angle between the two normal vectors is below 60 degrees. The 3D knot, growth surface geometries, and fiber patterns observed in this study clearly align with the patterns revealed by a previous micro-CT study. The method and data obtained are valuable for the subsequent development of a more refined and rigorously calibrated fiber angle model than those currently available.

Timber buildings, including hybrids, are increasingly popular due to their beneficial environmental aspects. During 2023-2024, the authors had a unique opportunity to perform ambient vibration testing of two six-story buildings with identical timber structures placed on top of different types of concrete substructures and with varying soil conditions. Ambient vibration tests were performed twice during the construction of each building. Finite element models were created for each vibration test, and their parameters were updated simultaneously to accurately simulate the buildings' natural frequencies and the corresponding mode shapes. The model updating process was facilitated by training four surrogate models to represent the four finite element models corresponding to the two buildings and the two construction stages, which were then verified against the finite element models. The updated models were used to investigate the effects of the in-plane shear stiffness of cross-laminated timber walls, soil-structure interaction, moisture content, and non-structural walls on the dynamic properties of hybrid timber-concrete buildings. The results showed nearly identical dynamic performance of the two buildings, suggesting that the differences in substructure and soil conditions do not affect the natural frequencies and mode shapes. A sensitivity analysis revealed that the in-plane shear stiffness of the CLT walls is the most significant factor affecting the modal properties of the two buildings.

Timber buildings, including hybrids, are increasingly popular due to their beneficial environmental aspects. During 2023-2024, the authors had a unique opportunity to perform ambient vibration testing of two six-story buildings with identical timber structures placed on top of different types of concrete substructures and with varying soil conditions. Ambient vibration tests were performed twice during the construction of each building. Finite element models were created for each vibration test, and their parameters were updated simultaneously to accurately simulate the building's natural frequencies and corresponding mode shapes. The model updating process was facilitated by training four surrogate models to represent the four finite element models corresponding to the two buildings and the two construction stages. The updated models were used to investigate the effects of the in-plane shear stiffness of cross-laminated timber (CLT) walls, soil-structure interaction, moisture content, and non-structural walls on the dynamic properties of hybrid timber-concrete buildings. The results showed nearly identical dynamic performance of the two buildings, suggesting that the differences in sub-structure and soil conditions do not affect the natural frequencies and mode shapes. A sensitivity analysis revealed that the in-plane shear stiffness of the CLT walls is the most significant factor affecting the modal properties of the two buildings. 

Fyrtornet, a pioneering 51-meter-tall mass timber office tower in Malmö, Sweden, is thefirst and tallest building of the Embassy of Sharing project. Fyrtornet’s lightweight naturemade wind-induced accelerations for occupant comfort a critical design factor. The designincorporated analytical assessments validated by wind tunnel testing, with mass distribu-tion and CLT core wall shear connections identified as key factors. Iterations led to the finaldesign with an optimised brazing system and added mass in the concrete roof slab so thatcomfort criteria were met.

Extensive ambient vibration testing at seven construction stages in combination with FE-model updating revealed that non-structural elements (screed and internal walls) signifi-cantly decreased natural frequencies, as the added mass outweighed the stiffness in-creases. The foundation exhibited rigid behaviour, and GLT connection stiffness ap-proached rigidity in the final stages, likely due to screed and partition walls. A permanentmonitoring system is now installed to track dynamic performance and environmental ef-fects. With this, Fyrtornet serves as an essential case study for the dynamics of tall timberbuildings

This paper introduces a novel framework for real-time, local-level condition assessment of structural members using vibration-based anomaly detection. The approach employs a pair of piezoelectric patches: one acting as an actuator to excite the structural member and another as a sensor to capture the resulting vibration response. These signals are processed by four different machine learning models: two supervised and two unsupervised. Supervised models are a onedimensional Convolutional Neural Network (1D CNN) and a hybrid Long Short-Term Memory model (1D CNN-LSTM). The unsupervised models are a one-dimensional Convolutional Autoencoder (1D CAE) and a hybrid 1D CAE-LSTM. The supervised algorithm basically classifies each signal segment as either "undamaged" or "damaged". By aggregating the classification outputs across multiple segments, a structural health index is derived to quantify deviations from the baseline response of an undamaged member. In the unsupervised models, anomaly detection is based on reconstruction errors, which compute a similar health index by measuring deviation from the undamaged baseline response. The developed methods have been experimentally validated on a tension rod assembly, with damage simulated by reducing the applied tension. In this setup, the rod is threaded through a hollow steel section, and tensile force is adjusted via wing nuts at both ends. The fully tightened assembly represents the "undamaged" state, while the loosened conditions are considered as the "damaged" states. The system effectively identifies and quantifies damage severity in real-time, generating a visual graph for intuitive tracking of structural health changes. These applications demonstrate the potential of this method for practical use in monitoring real-world structures, such as suspension bridge cables and prestressed concrete elements. Experimental results confirm that the health indices derived from the proposed method closely align with the actual damage severity applied to the assembly, highlighting their accuracy and reliability.

This paper proposes a one-dimensional convolutional long short-term memory (1D-CNN-LSTM) model for estimating the pith position and average ring width in Norway spruce timber boards. The model predicts these crosssectional parameters by processing sequences of light-intensity signals derived from optical scans of the board's four surfaces. The dataset used for training the model consists of synthetic boards sawn from simulated 3D logs. The model was evaluated on a dataset consisting of 552 end cross-sections from actual Norway spruce boards. Comparisons between the automatic and manual pith and ring width estimations demonstrated a very good accuracy. The computational speed of the model was more than twice as fast as the quickest method available in the literature. A large set of boards was then used to determine the advantages of incorporating the automatically determined average ring width in formulating indicating properties for machine strength grading. This evaluation revealed that the average ring width could, in certain situations, compensate for unknown variables such as density or resonance frequency in predicting the tensile and bending strength of Norway spruce boards.

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.