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Displacement-based design (DBD) methods were originally developed in the 1990s for concrete structures and later modified for light-frame wood buildings and mass timber buildings. DBD methods use displacement as the design criterion to minimize economic losses, control component damage, and enable rapid return to functionality for increased resilience. This paper introduces a performance-based design check procedure that uses the direct displacement-based design (DDD) method and numerical pushover analysis for the design of cross-laminated timber and light-frame shear (CLT-LiFS) buildings. Prestressing CLT panels with embedded posttensioning high-strength steel cables or external high-strength steel rods enable them to provide robust lateral load resistance and self-centering capacity. LiFS walls offer additional lateral load resistance and energy dissipation through the slip of nail connections. A six-story CLT-LiFS building was used as a design example meeting three performance levels (damage limitation, life safety, 15 and collapse prevention) based on ASCE/SEI 41-17 criteria. Each performance level corresponds to a set of three criteria: interstory drift limits, cable stress limits, and CLT compressive strain limits at the rocking interface. The design was checked using a series of nonlinear time history (NLTH) analyses with a suite of ground motion records. The results from the design check procedure and the statistical distributions of performance criteria from NLTH analysis confirmed that the six-story CLT-LiFS building met performance expectations. Additionally, a collapse fragility curve was developed to provide a comprehensive understanding of the building's behavior. [ABSTRACT FROM AUTHOR]

Prediction of pulp yield of Eucalyptus globulus wood samples based on partial least squares (PLS) regression can be optimized by utilizing specific near infrared (NIR) wavelengths. A critical feature of this approach is the weighting of constraint conditions. Equal weighting balances optimization in terms of calibration and prediction; however, there is a lack of knowledge regarding prediction performance of wood property models when different weight factors are used. In this study, pulp yield models were developed using two E. globulus data sets characterized by narrow (5%) and extreme (22.6%) yield ranges and represented by untreated and second derivative NIR spectra. The global optimization solver pySOT was used to optimize the performance of a PLS regression model in terms of wavelengths selected and number of latent variables. A linear function of R-squares for calibration ( R c 2 ) and prediction ( R p 2 ) sets was utilized as the objective function with the aim of maximizing α R c 2 + 1 - α R p 2 for all values of α between 0 (maximizing R p 2 without concern for R c 2 ) and 1 (only maximizing R c 2 ). Values of α ≤ 0.8 provided good predictive performance, whereas α ≥ 0.9 tended to overfit the calibration data indicating that models are robust for values of α from 0 to 0.8. Representative wavelengths for each data set were identified and assigned to corresponding wood components through a band assignment process. Strong agreement was observed for α ≤ 0.8 ; however, for α ≥ 0.9 , identified wavelengths generally occurred in regions unrelated to vibrations arising from specific wood components. [ABSTRACT FROM AUTHOR]

Near-infrared wavelengths selected by genetic algorithm were used to optimize partial least squares (PLS) regression models for loblolly pine (Pinus taeda L.) from the southeastern United States. Wood properties examined included density (D), microfibril angle, modulus of elasticity and tracheid coarseness (C), radial diameter (R), tangential diameter (T), and wall thickness (w)—measured by SilviScan. The optimization process was run for each property with Agenda 2020 samples utilized for PLS model development and the other sets used for prediction. The number of variables (i.e. wavelengths) varied from 10 to 100 with an optimum number identified by genetic algorithm. When compared to a full data set model (based on 700 wavelengths), calibration and prediction performance of optimized PLS regression models were superior for all properties. Importantly, representative wavelengths for each property were consistently related to recognized bond vibrations observed in specific wood components demonstrating that optimization targets wavelengths directly related to changes in wood chemistry within the examined loblolly pine samples. [ABSTRACT FROM AUTHOR]