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Hybrid Building Energy-Structure Performance Analysis
Abstract
Sustainability and resilience are becoming major objectives of the future built environment. Although each of them has been developed independently, there is limited research on holistic integration. This dissertation (from a hybrid building energy-structure viewpoint) aims to address the challenge. To achieve this, a systematic study is performed, which includes elaborating fundamental relationships/interactions between energy/carbon and structural performance indicators based on different building types and features, bridging technical gaps of integrated and automated multi-physics simulations, and developing analytic methods, including data analysis and Machine Learning under this multidisciplinary context.
The dissertation is divided into two primary parts. Part I is about the sensitivity analysis of Operating Energy (OE) and general structural performance for high-rise commercial shear wall concrete framed buildings. A novel framework of sensitivity analysis, including initial studies and adaptive global sensitivity analysis (Scaled Morris Method), is developed based on an automated multi-physics simulation platform using APIs of “Python-EnergyPlus-Abaqus.” More than 2000 models, including two types of façades are simulated. It is highly noted that sensitivity indices µ *()scale and σ(n)cale are successful for quantifying and comparing the sensitivity and nonlinearity/interaction effects of features, including zone/structure layout, envelope types, and inherited parameters.
Part II mainly includes 1) fully constrained multi-objective optimization of buildings’ OE and Initial Embodied Carbon (EC) considering both Initial Investment Cost and Failure Rate of wind/seismic structural design and 2) simultaneous predictions of building’s OE and Initial EC among four geographic locations. Correspondingly, a novel hybrid method combined with a Genetic Algorithm and Pareto Analysis and a comprehensive study of six Multi-Task Learning models considering effects of tasks, features, and training ratios are proposed, respectively. To collect small datasets, the Rhino/Grasshopper/Python tool integrating Ladybug/Honeybee and SAP2000/GeometryGym/EC3 for archetype mid-rise residential concrete MRF buildings is developed. The results demonstrate that 1) the Initial CUI has strong linear relationships with EUI, and optimal geometries of Pareto front sets always have higher compactness, lower average slenderness ratio, and aspect ratios nearly equaling one, and 2) Trace-Norm and Sparse-Low Rank within latent geometry features, and higher training ratios are the best two models for the generalization performance.
Subject
Multi-disciplinary StudyMulti-physics Simulation
Building Energy Simulation
Parametric Modeling
Building Energy Efficiency
Structural Analysis
Finite Element Analysis
Sustainable Structural Design
Global Sensitivity Analysis
Morris Method
Initial Embodied Carbon
Operating Energy
Multi-objective Optimization
Geometry-based Optimization
Genetic Algorithm
Data-driven Building Energy Prediction
Data Analytics
Machine Learning
Multi-Task Learning
Citation
Shen, Yang (2022). Hybrid Building Energy-Structure Performance Analysis. Doctoral dissertation, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /197982.
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