The feasibility of a portable NIR sensor for off-line determination of diverse wood quality aspects relevant in the production of glue-laminated timber was demonstrated. The best performance was noticed for assessing wood moisture content, with a lower capacity to estimate wood density and mechanical properties. NIR spectroscopy was modestly capable of predicting surface roughness. However, the traceability of the raw resources and the automatic classification of diverse wood defects were successfully demonstrated. The developed chemometric model could predict the total delamination and detailed delamination length. Finally, recommendations regarding further system development were provided with the aim of implementation and integration of the NIR measurement into glue-laminated timber production plants.
Mass timber construction systems, incorporating engineered wood products as structural elements, are gaining acceptance as a sustainable alternative to multi-story concrete or steel-frame structures. The relative novelty of these systems brings uncertainties on whether these buildings perform long-term as expected. Consequently, several structural health monitoring (SHM) projects have recently emerged to document their behavior. A wide and systematic use of this data by the mass timber industry is currently hindered by limitations of SHM programs. These limitations include scalability, difficulty of data integration, diverse strategies for data collection, scarcity of relevant data, complexity of data analysis, and limited usability of predictive tools. This perspective paper envisions the use of avatars as a Web-based layer on top of sensing devices to support SHM data and protocol interoperability, analysis, and reasoning capability and to improve life cycle management of mass timber buildings. The proposed approach supports robustness, high level and large-scale interoperability and data processing by leveraging the Web protocol stack, overcoming many limitations of conventional centralized SHM systems. The design of avatars is applied in an exemplary scenario of hygrothermal data reconstruction, and use of this data to compare different mold growth prediction models. The proposed approach demonstrates the ability of avatars to efficiently filter and enrich data from heterogeneous sensors, thus overcoming problems due to data gaps or insufficient spatial distribution of sensors. In addition, the designed avatars can provide prediction or reasoning capability about the building, thus acting as a digital twin solution to support building lifecycle management.