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Evolutionary topology optimization of continuum structures
Evolutionary Topology Optimization of Continuum Structures treads new ground with a comprehensive study on the techniques and applications of evolutionary structural optimization (ESO) and its later version bi-directional ESO (BESO) methods.
eBook
Mechanical Behavior and Design Properties of Ultra-High- Performance Concrete (Open Source)
The appropriate and efficient design of structural components made with ultra-high-performance concrete (UHPC) requires the establishment of key design properties and material models that engage UHPC's distinct mechanical properties, as compared to conventional concrete. This paper presents the results of an extensive program of compression and tension property assessment executed according to existing testing methods to assess the mechanical characteristics of several commercially available UHPC products. The experimental results are then used to propose suitable mechanical models and design parameters that are foundational for the structural-level application of UHPC. The models rely on a set of experimentally identified mechanical performance properties that distinguish UHPC from conventional concrete and establish the basis of the material qualification for use in structural design. As such, this work constitutes a fundamental step in ongoing efforts to develop UHPC structural design guidance in the United States. Keywords: compression properties; mechanical models; structural design parameters; tension properties; ultra-high-performance concrete (UHPC).
Journal Article
Integrated Schematic Design Method for Shear Wall Structures: A Practical Application of Generative Adversarial Networks
The intelligent design method based on generative adversarial networks (GANs) represents an emerging structural design paradigm where design rules are not artificially defined but are directly learned from existing design data. GAN-based methods have exhibited promising potential compared to conventional methods in the schematic design phase of reinforced concrete (RC) shear wall structures. However, for the following reasons, it is challenging to apply GAN-based approaches in the industry and to integrate them into the structural design process. (1) The data form of GAN-based methods is heterogeneous from that of the widely used computer-aided design (CAD) methods, and (2) GAN-based methods have high requirements on the hardware and software environment of the user’s computer. As a result, this study proposes an integrated schematic design method for RC shear wall structures, providing a workable GAN application strategy. Specifically, (1) a preprocessing method of architectural CAD drawings is proposed to connect the GAN with the upstream architectural design; (2) a user-friendly cloud design platform is built to reduce the requirements of the user’s local computer environment; and (3) a heterogeneous data transformation method and a parametric modeling procedure are proposed to automatically establish a structural analysis model based on GAN’s design, facilitating downstream detailed design tasks. The proposed method makes it possible for the entire schematic design phase of RC shear wall structures to be intelligent and automated. A case study reveals that the proposed method has a heterogeneous data transformation accuracy of 97.3% and is capable of generating shear wall layout designs similar to the designs of a competent engineer, with 225 times higher efficiency.
Journal Article
Machine Learning-Based Modeling for Structural Engineering: A Comprehensive Survey and Applications Overview
Modeling and simulation have been extensively used to solve a wide range of problems in structural engineering. However, many simulations require significant computational resources, resulting in exponentially increasing computational time as the spatial and temporal scales of the models increase. This is particularly relevant as the demand for higher fidelity models and simulations increases. Recently, the rapid developments in artificial intelligence technologies, coupled with the wide availability of computational resources and data, have driven the extensive adoption of machine learning techniques to improve the computational accuracy and precision of simulations, which enhances their practicality and potential. In this paper, we present a comprehensive survey of the methodologies and techniques used in this context to solve computationally demanding problems, such as structural system identification, structural design, and prediction applications. Specialized deep neural network algorithms, such as the enhanced probabilistic neural network, have been the subject of numerous articles. However, other machine learning algorithms, including neural dynamic classification and dynamic ensemble learning, have shown significant potential for major advancements in specific applications of structural engineering. Our objective in this paper is to provide a state-of-the-art review of machine learning-based modeling in structural engineering, along with its applications in the following areas: (i) computational mechanics, (ii) structural health monitoring, (iii) structural design and manufacturing, (iv) stress analysis, (v) failure analysis, (vi) material modeling and design, and (vii) optimization problems. We aim to offer a comprehensive overview and provide perspectives on these powerful techniques, which have the potential to become alternatives to conventional modeling methods.
Journal Article
Interface Shear of Ultra-High-Performance Concrete (Open Source)
Due to its distinct mechanical properties, ultra-high-performance concrete (UHPC) behaves differently than conventional concrete when subjected to interface shear demands. The use of UHPC in primary structural elements, for which interface shear resistance can be an important structural response, is growing as UHPC-class materials become more readily available. Structural design provisions for the interface shear capacity of UHPC are needed. This research conducted 11 interface shear tests of monolithically cast UHPC and combined those results with other tests from the literature to develop a predictive model for UHPC. The interface shear capacity was studied by conducting tests with steel reinforcement of varying yield stress and reinforcement ratios at the interface. A predictive model was developed indicating that the total tensile resistance across the shear interface is a critical parameter in determining the peak shear resistance. This includes the tensile resistance of both the reinforcing steel and UHPC. Design guidance for the interface shear resistance of UHPC is also proposed. Keywords: interface shear; structural design parameters; tensile behavior; ultra-high-performance concrete (UHPC).
Journal Article