Increasing awareness of sustainable building materials has led to interest in enhancing the structural performance of engineered wood products. Wood is a sustainable, renewable material, and the increasing use of wood in construction contributes to its sustainability. Multi-layer wooden panels are one type of engineered wood product used in construction.

There are various techniques to assemble multi-layer wooden panels into prefabricated, load-bearing construction elements. Assembly techniques considered in the earliest stages of this research work were laminating, nailing, stapling, screwing, stress laminating, doweling, dovetailing, and wood welding. Cross-laminated timber (CLT) was found to offer some advantages over these other techniques. It is cost-effective, not patented, offers freedom of choice regarding the visibility of surfaces, provides the possibility of using different timber quality in the same panel at different points of its thickness, and is the most well-established assembly technique currently used in the industrial market.

Building upon that foundational work, the operational capabilities of CLT were further evaluated by creating panels with different layer orientations. The mechanical properties of CLT panels constructed with layers angled in an alternative configuration produced on a modified industrial CLT production line were evaluated. Timber lamellae were adhesively bonded in a single-step press procedure to form CLT panels. Transverse layers were laid at a 45° angle instead of the conventional 90° angle with respect to the longitudinal layers’ 0° angle.

Tests were carried out on 40 five-layered CLT panels, each with either a ±45° or a 90° configuration. Half of these panels were evaluated under bending: out-of-plane loading was applied in the principal orientation of the panels via four-point bending. The other twenty were evaluated under compression: an in-plane uniaxial compressive loading was applied in the principal orientation of the panels. Quasi-static loading conditions were used for both in- and out-of-plane testing to determine the extent to which the load-bearing capacity of such panels could be enhanced under the current load case. Modified CLT showed higher stiffness, strength, and fifth-percentile characteristics, values that indicate the load-bearing capacity of these panels as a construction material. Failure modes under in- and out-of-plane loading for each panel type were also assessed.

Data from out-of-plane loading were further analysed. A non-contact full-field measurement and analysis technique based on digital image correlation (DIC) was utilised for analysis at global and local scales. DIC evaluation of 100 CLT layers showed that a considerable part of the stiffness of conventional CLT is reduced by the shear resistance of its transverse layers. The presence of heterogeneous features, such as knots, has the desirable effect of reducing the propagation of shear fraction along the layers. These results call into question the current grading criteria in the CLT standard. It is suggested that the lower timber grading limit be adjusted for increased value-yield.

The overall experimental results suggest the use of CLT panels with a ±45°-layered configuration for construction. They also motivate the use of alternatively angled layered panels for more construction design freedom, especially in areas that demand shear resistance. In addition, the design possibility that such 45°-configured CLT can carry a given load while using less material than conventional CLT suggests the potential to use such panels in a wider range of structural applications. The results of test production revealed that 45°-configured CLT can be industrially produced without using more material than is required for construction of conventional 90°-configured panels. Based on these results, CLT should be further explored as a suitable product for use in more wooden-panel construction.

Innovations in materials science techniques are essential for advancing sustainable construction materials and achieving resource efficiency. Ideally, these novel approaches should replace traditional single-localised measurements with multidimensional, full-field data-fusion methods. A multi-sensor and data-driven approach can reveal hidden patterns and correlations in datasets that conventional methods might overlook. Guided by a vision to create a framework for material- and application-independent assessment, the work described here demonstrated a comprehensive approach. By prioritising the extension of measurement technologies, efforts were directed towards applications for cross-laminated timber (CLT), combined with material processing.

Hypothesis: An industrially feasible, performance-driven assembly strategy for CLT layers can be developed by integrating correlated full-field mechanics, with non-contact multi-modal methods based on artificial intelligence (AI) tools driven through machine learning (ML), to extract insights into variables that govern material mechanics. Basing this work on multidimensional image-based techniques will lead to a more complete understanding of the mechanical behaviour of composite materials such as CLT.

Novel applications of measurement techniques, together with innovative combinations of both measurement techniques and data processing methods, have been developed, to use less resources and provide added functionality to CLT. For this, various techniques for evaluating material properties have been investigated and compared. Digital image correlation (DIC) was used for full-field mechanics assessment and was applied in various formats, including static, quasi-static, cyclic, and high-speed experiments. DIC improved displacement and strain measurements across different scales, from full-scale sections down to tenths of millimetres, and made it possible to differentiate finer wood features with greater spatial and temporal resolution. CLT was subjected to loading conditions including in-plane compression and out-of-plane bending, which were investigated. Displacement and strain were assessed concerning longitudinal and transverse interlayer interactions, taking into account bonded or non-bonded adhesive interfaces, as well as natural features like knots, fibre deviations, heartwood, and sapwood. CLT with a ±45°-layered configuration provided greater stiffness and strength than conventional configurations, particularly in regions where greater shear resistance was required. Knots, traditionally seen as structural weaknesses in CLT, have here been shown to improve the shear resistance in transverse layers. A reduction in shear propagation in knotty timber means that it may be possible to revise timber standards to support the use of materials previously considered unsuitable for structural applications.

Multi-sensor data fusion was applied in material processing such as cutting, and DIC merged with chemical analysis revealed surface-densified set-recovery differences between earlywood and latewood in growth rings. Three different techniques – thermoelastic stress analysis (TSA) using thermal imaging data, the grid technique for tracking finer deformation patterns, and DIC – were augmented with finite element (FE) methods and compared with respect to their effectiveness in stress estimation. Multispectral and hyperspectral imaging were used to capture chemical and physical properties, localised measurements were made with a point-based near-infrared (NIR) probe, and full-field imaging was used to capture pixel-level data. Four-dimensional X-ray computed tomography (CT) was used on CLT to monitor internal moisture absorption and desorption. Hyperspectral NIR imaging in combination with X-ray imaging was used to classify the material properties of CLT. NIR was also used for mould detection and to compare the efficiency of CLT edge-sealing compounds. Moisture and its role in material processing were explored, and absorption and desorption experiments revealed that the edge-sealing of CLT could reduce the risk of mould formation.

Experimental procedures were optimised with factorial designs. MATLAB was integrated by generative AI-supported code generation and decentralised high-performance data processing. To enhance material assessment, multivariate data analysis (MVDA) and multivariate image analysis (MIA) were applied for multi-modal analysis by means of principal component analysis (PCA) and projection to latent structures – partial least squares discriminant analysis (PLS-DA). Hierarchical clustering analysis (HCA) was used to classify the impact of different clusters on modelling. Deep learning with a convolutional neural network (CNN) extracted additional refined features from the wood structure.

Mechanical properties were derived using multi-modal integration of image-based data, demonstrating the potential of combining traditional principles with modern technology to uncover previously unknown material characteristics. By integrating measurement techniques and incorporating AI, the work described in this thesis enhances material performance and resource efficiency in composite materials, in particular CLT.

Innovationer inom materialvetenskapliga tekniker är essentiella för att främja hållbara byggmaterial och resurseffektivitet. Idealiskt bör dessa nya tillvägagångssätt ersätta traditionella enskilda lokaliserade mätningar med multidimensionella, heltäckande datafusionstekniker. En multisensorisk och datadriven ansats kan avslöja dolda mönster och korrelationer i datamängder som konventionella metoder kan förbise. Med en vision att skapa ett ramverk för material- och applikationsoberoende bedömning, demonstrerade detta arbete en omfattande metod. Genom att prioritera utökningen av mätteknologier riktades insatser mot applikationer för korslimmat trä (KL-trä), integrerat med materialbearbetning.

Hypotes: En industriellt genomförbar, prestandadriven sammanfogningsstrategi för KL-trä-lager kan utvecklas genom att integrera korrelerad heltäckande mekanik med beröringsfria multimodala metoder baserade på verktyg inom artificiell intelligens (AI) drivna av maskininlärning (ML), för att extrahera insikter om variabler som styr materialmekanik. Att basera detta arbete på multidimensionella, bildbaserade tekniker kommer att leda till en mer fullständig förståelse av det mekaniska beteendet hos kompositmaterial såsom KL-trä.

Nya tillämpningar av mättekniker, tillsammans med innovativa kombinationer av både mättekniker och databehandlingsmetoder, har utvecklats för att använda färre resurser och ge ökad funktionalitet till KL-trä. För detta ändamål har olika tekniker för att utvärdera materialegenskaper undersökts och jämförts. Digital bildkorrelation (DIC) användes för bedömning av heltäckande mekanik och tillämpades i olika format, inklusive statiska, kvasi-statiska, cykliska och höghastighetsexperiment. DIC förbättrade mätningar av förskjutning och töjning över olika skalor, från fullskaliga sektioner ner till tiondels millimetrar, och möjliggjorde att särskilja detaljerade trästrukturer med förbättrad rumslig och tidsmässig upplösning. KL-trä utsattes för lastförhållanden som inkluderade kompression i planet och böjning vinkelrätt mot planet, vilka undersöktes. Förskjutning och töjning bedömdes i longitudinella och tvärgående interlagerinteraktioner, inklusive limmade eller olimmade limfogar, samt naturliga egenskaper som kvistar, fiberavvikelser, kärnved och splintved. KL-trä med en ±45°-lagerkonfiguration uppvisade större styvhet och styrka än konventionella konfigurationer, särskilt i områden där större skjuvmotstånd krävdes. Kvistar, som traditionellt betraktas som strukturella svagheter i KL-trä, har här visat sig förbättra skjuvmotståndet i tvärgående lager. En minskning av skjuvutbredning i kvistigt trä antyder att det kan vara möjligt att revidera trästandarder för att stödja användningen av material som tidigare ansetts olämpliga för strukturella applikationer.

Multisensorisk datafusion tillämpades i materialbearbetning såsom skärning, och DIC kombinerat med kemisk analys avslöjade skillnader i återgång efter ytdensifiering mellan vårved och sommarved i årsringar. Tre olika tekniker – termoelastisk stressanalys (TSA) med hjälp av termisk bilddata, grid-tekniken för att spåra finare deformationsmönster, och DIC – förstärktes med finita elementmetoder (FE) och jämfördes avseende deras effektivitet i stressestimering. Multispektral och hyperspektral avbildning användes för att fånga kemiska och fysiska egenskaper; lokaliserade mätningar utfördes med en punktbaserad nära-infraröd (NIR) sond, och heltäckande avbildning användes för att fånga pixelnivådata. Fyrdimensionell röntgendatortomografi (CT) användes på KL-trä för att analysera intern fuktabsorption och fuktavgivning. Hyperspektral NIR-avbildning i kombination med röntgenavbildning användes för att klassificera materialegenskaperna hos KL-trä. NIR användes också för mögeldetektion och för att jämföra effektiviteten hos kantförseglingsföreningar för KL-trä. Fukt och dess roll i materialbearbetning utforskades, och absorption- och desorptionsexperiment visade att kantförsegling av KL-trä kan minska risken för mögelbildning.

Experimentella procedurer optimerades genom faktoriell design. MATLAB integrerades genom generativ AI-understödd kodgenerering och decentraliserad högpresterande databehandling. För att förbättra materialbedömningen tillämpades multivariat dataanalys (MVDA) och multivariat bildanalys (MIA) för multimodal analys genom huvudkomponentanalys (PCA) och projektion till latenta strukturer – partiell minstakvadraters diskriminantanalys (PLS-DA). Hierarkisk klusteranalys (HCA) användes för att klassificera påverkan av olika kluster på modellering. Djupinlärning med ett konvolutionellt neuralt nätverk (CNN) extraherade ytterligare förfinade egenskaper från trästrukturen.

Mekaniska egenskaper härleddes genom multimodal integration av bildbaserade data, vilket demonstrerade potentialen i att kombinera traditionella principer med modern teknik för att upptäcka tidigare okända materialegenskaper. Genom att integrera mättekniker och inkorporera AI, förbättrar arbetet som beskrivs i denna avhandling materialprestanda och resurseffektivitet i kompositmaterial, med särskilt fokus på KL-trä.