Visual-based classification models for grading reclaimed structural timber for reuse: A theoretical, numerical and experimental investigation

Among the key benefits of using structural timber is its potential for reuse after being dismantled from an existing building. Recycling and reuse are central concepts in the circular economy. However, the installation and dismantling of structural elements often leave traces from previous use, such as holes from connectors like dowels or screws, internal piping and cabling. Therefore, it is crucial to develop methods to rigorously quantify the reduced load-bearing capacity of recycled beams due to potential holes using efficient and expedited methods akin to visual grading approaches. This work proposes a visual-based method for classifying recycled timber based on the geometric characteristics of the artificial holes. A stochastic mechanics-based numerical model was developed to predict the bending strength reduction of beams with random hole patterns and thus generate an extensive dataset for calibrating data-driven binary classification models. Machine learning and conditional classification models are used to determine if the reduction in bending strength is greater or less than 20%, being the predefined threshold value for reduced strength grading. An experimental campaign on timber beams with specific hole patterns, determined after experimental design, led to the numerical model validation and the calibration of thresholds for the conditional classification model, which relies on a single feature: the sum of the diameters of the holes in two beam regions. The study shows that with an elementary conditional model, high-performance metrics of the binary classification model comparable to machine learning techniques can be achieved. In other words, with a balanced dataset, accuracies over 80% in classifying the level of capacity reduction, greater or lesser than 20%, can be achieved simply by comparing the sum of diameters to a predetermined threshold. This method currently fills a regulatory and methodological gap in safely reusing structural timber. • Visual-based grading of recovered timber beams. • Comparison of visual-based classification models for structural grading. • Development of a simplified mechanics-based procedure for bending capacity assessment. • Generation of a dataset for model calibration using a stochastic Finite element model. • Experimental validation of the proposed mechanics-based procedure.