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Engaging with performance feedback in early building design often involves building a custom parametric model and generating large datasets, which is not always feasible. Alternatively, large parametric datasets of general design problems and filtering methods could be used together to explore specific design decisions. This paper investigates the generalizability of a method that dynamically assesses variable importance and likely influence on performance objectives as a precomputed design space is filtered down. The method first trains linear model trees to predict building performance objectives across a generic design space. Leaf node models are then aggregated to provide feedback on variable importance in different design space regions. This approach is tested on three design problems that vary in number of variables, samples, and design space structure to reveal advantages and potential limitations of the method. Algorithm improvements are proposed, and general recommendations are developed to apply it on future datasets. 

Parametric optimization techniques allow building designers to pursue multiple performance objectives, which can benefit the overall design. However, the strategies used by architecture and engineering graduate students when working with optimization tools are unclear, and ineffective computational design procedures may limit their success as future designers. In response, this re-search identifies several designerly behaviors of graduate students when responding to a conceptual building design optimization task. It uses eye-tracking, screen recording, and empirical methods to code their behaviors following the situated FBS framework. From these data streams, three different types of design iterations emerge: one by the designer alone, one by the optimizer alone, and one by the designer incorporating feedback from the optimizer. Based on the timing and frequency of these loops, student participants were characterized as completing partial, crude, or complete optimization cycles while developing their designs. This organization of optimization techniques establishes reoccurring strategies employed by developing designers, which can encourage future pedagogical approaches that empower students to incorporate complete optimization cycles while improving their designs. It can also be used in future research studies to establish clear links between types of design optimization behavior and design quality. 

Numerical analyses can aid design exploration, but there are several computational approaches available to consider design options. These range from “brute-force” search to optimization. However, the implementation of optimization can be challenging for the complex, time-intensive analyses required to assess seismic performance. In response to this challenge, this study tests several optimization strategies for the direct displacement-based design of a lateral force-resisting system (LFRS) using mass timber panels with U-shaped flexural plates (UFPs) and post-tensioning high-strength steel rods. The study compares two approaches: (1) a brute-force sampling of designs and data filtering to determine acceptable solutions; and (2) various automated optimization algorithms. The differential evolution algorithm was found to be the most efficient and robust approach, saving 90% of computational cost compared to bruteforce sampling while producing comparable solutions. However, every optimization formulation did not return best range of design options, often requiring reformulation or hyperparameter tuning to ensure effectiveness. 

Numerical analyses can aid design exploration, but there are several computational approaches available to consider design options. These range from “brute-force” search to optimization. However, the implementation of optimization can be challenging for the complex, time-intensive analyses required to assess seismic performance. In response to this challenge, this study tests several optimization strategies for the direct displacement-based design of a lateral force-resisting system (LFRS) using mass timber panels with U-shaped flexural plates (UFPs) and post-tensioning high-strength steel rods. The study compares two approaches: (1) a brute-force sampling of designs and data filtering to determine acceptable solutions, and (2) various automated optimization algorithms. The differential evolution algorithm was found to be the most efficient and robust approach, saving 90% of computational cost compared to brute-force sampling while producing comparable solutions. However, every optimization formulation did not return best range of design options, often requiring reformulation or hyperparameter tuning to ensure effectiveness. 

This paper describes the lateral force resisting system (LFRS) design in a full-scale six-story shake-table test building and presents a comparative cradle-to-grave life-cycle assessment of alternative LFRSs. The test building features the reuse of material from a ten-story shake-table structure comprised of engineered mass timber (MT) products. These include MT floors (cross-, glue-, nail-, and dowel-laminated timber [CLT], [GLT], [NLT], [DLT]); MT posttensioned rocking walls (CLT and mass ply panels [MPP]); and a gravity system consisting of laminated-veneer lumber (LVL) beams and columns. Shake-table testing will benchmark innovative, low-damage design solutions for the LFRSs. To supplement this test, the environmental impact of a MT LFRS is determined relative to design alternatives that use conventional materials. The Athena Impact Estimator for Buildings was used to perform a comparative, cradle-to-grave life-cycle assessment (LCA) of the prototype MT LFRS with respect to an alternative, functionally equivalent reinforced concrete (RC) shear wall design. The LCA results showed reduced environmental impacts across some impact metrics, with a significant reduction in Global Warming Potential for the MT LFRS when accounting for biogenic carbon.