Biogeography-Based Optimization of RC structures including static
soil-structure interaction
- URL: http://arxiv.org/abs/2103.05129v1
- Date: Mon, 8 Mar 2021 22:48:04 GMT
- Title: Biogeography-Based Optimization of RC structures including static
soil-structure interaction
- Authors: I.A. Negrin, D. Roose, E.L. Chagoyen, G. Lombaert
- Abstract summary: We present a method to minimize the cost of the structural design of reinforced concrete structures using Biogeography-Based Optimization.
SAP2000 is used as computational engine, taking into account modelling aspects such as static soil-structure interaction (SSSI)
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: A method to minimize the cost of the structural design of reinforced concrete
structures using Biogeography-Based Optimization, an evolutionary algorithm, is
presented. SAP2000 is used as computational engine, taking into account
modelling aspects such as static soil-structure interaction (SSSI). The
optimization problem is formulated to properly reflect an actual design
problem, limiting e.g. the size of reinforcement bars to commercially available
sections. Strategies to reduce the computational cost of the optimization
procedure are proposed and an extensive parameter tuning was performed. The
resulting tuned optimization algorithm allows to reduce the direct cost of the
construction of a particular structure project with 21% compared to a design
based on traditional criteria. We also evaluate the effect on the cost of the
superstructure when SSSI is takeninto account.
Related papers
- Quantum annealing-based structural optimization with a multiplicative design update [0.0]
This paper presents a new structural design framework, developed based on iterative optimization via quantum annealing (QA)
The novelty lies in its successful design update using an unknown design multiplier obtained by iteratively solving the optimization problems with QA.
To align with density-based approaches in structural optimization, multipliers are multiplicative to represent design material and serve as design variables.
arXiv Detail & Related papers (2024-06-27T02:07:38Z) - Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
We identify and test new hybrid architectures constructed from a variety of computational primitives.
We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis.
We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
arXiv Detail & Related papers (2024-03-26T16:33:12Z) - Scaling Pre-trained Language Models to Deeper via Parameter-efficient
Architecture [68.13678918660872]
We design a more capable parameter-sharing architecture based on matrix product operator (MPO)
MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts.
Our architecture shares the central tensor across all layers for reducing the model size.
arXiv Detail & Related papers (2023-03-27T02:34:09Z) - Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures.
This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead.
We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
arXiv Detail & Related papers (2023-03-16T21:06:13Z) - Physics-Constrained Neural Network for Design and Feature-Based
Optimization of Weave Architectures [0.6144680854063939]
We present a novel Physics-Constrained Neural Network (PCNN) to predict the mechanical properties of weave architectures.
We show that the proposed PCNN can effectively predict weave architecture for the desired modulus with higher accuracy than several baseline models considered.
arXiv Detail & Related papers (2022-09-19T16:16:45Z) - Parameter Tuning Strategies for Metaheuristic Methods Applied to
Discrete Optimization of Structural Design [0.0]
This paper presents several strategies to tune the parameters of metaheuristic methods for (discrete) design optimization of reinforced concrete (RC) structures.
A novel utility metric is proposed, based on the area under the average performance curve.
arXiv Detail & Related papers (2021-10-12T17:34:39Z) - Robust Topology Optimization Using Variational Autoencoders [2.580765958706854]
In this work, we use neural network surrogates to enable a faster solution approach via surrogate-based optimization.
We also build a Variational Autoencoder (VAE) to transform the high dimensional design space into a low dimensional one.
The resulting gradient-based optimization algorithm produces optimal designs with lower robust compliances than those observed in the training set.
arXiv Detail & Related papers (2021-07-19T20:40:51Z) - Conservative Objective Models for Effective Offline Model-Based
Optimization [78.19085445065845]
Computational design problems arise in a number of settings, from synthetic biology to computer architectures.
We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs.
COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
arXiv Detail & Related papers (2021-07-14T17:55:28Z) - Efficient Micro-Structured Weight Unification and Pruning for Neural
Network Compression [56.83861738731913]
Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices.
Previous unstructured or structured weight pruning methods can hardly truly accelerate inference.
We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
arXiv Detail & Related papers (2021-06-15T17:22:59Z) - An AI-Assisted Design Method for Topology Optimization Without
Pre-Optimized Training Data [68.8204255655161]
An AI-assisted design method based on topology optimization is presented, which is able to obtain optimized designs in a direct way.
Designs are provided by an artificial neural network, the predictor, on the basis of boundary conditions and degree of filling as input data.
arXiv Detail & Related papers (2020-12-11T14:33:27Z) - Learning to simulate and design for structural engineering [0.0]
In this work, we formulate the building structures as graphs and create an end-to-end pipeline that can learn to propose the optimal cross-sections of columns and beams.
The performance of the proposed structural designs is comparable to the ones optimized by genetic algorithm (GA) with all the constraints satisfied.
arXiv Detail & Related papers (2020-03-20T05:00:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.