Search for: All records

In architecture and engineering, design professionals may use the term “optimization” to describe a range of design approaches. These working definitions of optimization may not align with one another, or with the formal definition of mathematical optimization in engineering education. This paper presents a thematic analysis of 13 interviews with design professionals who use optimization in their work. Using the communication theory of coordinated management of meaning (CMM) to understand how the interviewer and interviewee were negotiating possible definitions, four themes are identified: optimization as performance improvement, as achieving varied goals, as a systematic process, and as a formal problem structure with variables and objectives, which is most aligned with the mathematical definition. Interviewees used these varied definitions dynamically in conversation, which informs researchers and educators about their potential use in practice. 

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. 

Abstract Permafrost influences 25% of land in the Northern Hemisphere, where it stabilizes the ground beneath communities and infrastructure and sequesters carbon. However, the coevolution of permafrost, river dynamics, and vegetation in Arctic environments remains poorly understood. As rivers meander, they erode the floodplain at cutbanks and build new land through bar deposition, creating sequences of landforms with distinct formation ages. Here we mapped these sequences along the Koyukuk River floodplain, Alaska, analyzing permafrost occurrence, and landform and vegetation types. We used radiocarbon and optically stimulated luminescence (OSL) dating to develop a floodplain age map. Deposit ages ranged from modern to 10 ka, with more younger deposits near the modern channel. Permafrost rapidly reached 50% areal extent in all deposits older than 200 years then gradually increased up to ∼85% extent for deposits greater than 4 Kyr old. Permafrost extent correlated with increases in black spruce and wetland abundance, as well as increases in permafrost extent within wetland, and shrub and scrub vegetation classes. We developed an inverse model to constrain permafrost formation rate as a function of air temperature. Permafrost extent initially increased by ∼25% per century, in pace with vegetation succession, before decelerating to <10% per millennia as insulating overbank mud and moss slowly accumulated. Modern permafrost extent on the Koyukuk floodplain therefore reflects a dynamic balance between widespread, time‐varying permafrost formation and rapid, localized degradation due to cutbank erosion that might trigger a rapid loss of permafrost with climatic warming. 

According to a new design paradigm called Converging Design, high-level optimization objectives such as resilience and sustainability can be pursued through iterative simulation and feedback. Unlike traditional design processes that prioritize desirable seismic performance at various seismic hazard levels, the Converging Design methodology also considers the long-term ecological impact of construction and functional recovery. This methodology requires navigating competing priorities, which can be pursued through multiobjective optimization (MOO). However, computational costs and incorporating uncertainty in seismic analysis also demand that optimization frameworks use algorithms and analysis resolutions that are appropriate to the decisions being made as the design is refined. While such a framework could be applied to any material, mass timber systems are increasingly attractive as a potential sustainable solution for buildings. In this study, using a Python-based object-oriented program, an automated structural design procedure is developed to evaluate the seismic and sustainability performance of parametrically definable mass timber building configurations. Different geometric classes with Cross-Laminated Timber Rocking Walls are modeled using OpenSees and are automatically designed. Their behavior is then studied to provide insights into the relationship between structural variables and the optimization objectives. The results show a clear trade-off between Seismic Safety (the inverse of risk) and Global Warming Potential due to the construction of different design options, although the nature of this trade-off depends on the desired seismic behavior limit states. The developed software thus enables designers to efficiently explore a range of early design options for mass timber lateral systems and to achieve optimal solutions that balance seismic and sustainability performance. 

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. 

To address functional recovery after earthquakes, there is growing interest in developing enhancedperformance seismic-resisting systems. Rocking walls, featuring a base gap-opening mechanism and designed to remain essentially elastic above the base, have demonstrated their potential in various construction materials, including mass timber. If combined with steel energy dissipators, the resulting hybrid steel-mass timber rocking walls have emerged as a promising seismic-resisting system. This study focuses on Post-Tensioned Mass Timber Rocking Walls supplemented with Buckling-Restrained Brace (BRB) boundary elements and builds upon findings from experimental programs funded by the National Science Foundation (NSF) and the United States Department of Agriculture (USDA). The rocking mechanism, controlled by the BRBs and the Post-Tensioned (PT) rods, provides self-centering behaviour, reducing the potential for residual drifts and improving post-earthquake repairability. An estimating method for higher-mode loading profiles is proposed and applied to a six-story archetype, which was tested at the Large High Performance Outdoor Shake Table (LHPOST) at the University of California San Diego (UCSD) in January 2024 as part of the NHERI Converging Design Project. The estimating method is practically formulated to facilitate the implementation in design procedures.