Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation

Despite the unprecedented permeation of Building Information Modelling (BIM) and collaboration platforms, architects and structural engineers, for the most part, act as separate entities. That is, linking architectural models with that of structural engineers remains labour-intensive and cumbersome; structural engineers can generate few alternatives for any given architectural model. This paper is an attempt to address these problems. It provides an automated procedure, computational details and the configuration of toolsets in the form of a proof of concept (PoC) prototype that: binds architectural models with structural ones for tall buildings; automatically generates and updates alternatives for structural models, based on the input extracted from the architectural model; and provides engineers with an optimum design that fulfils the set criteria. The prototype is developed based on an initial need assessment study -to determine the requirements of practitioners. The practicality of the solution is assessed in a case project. The paper contributes to the field by presenting ones of the first solutions capable of automated generation of optimised structural design based on architectural models. Designed for tall buildings to incorporate immense complexity, the solution acts as a blueprint in developing similar systems for simpler buildings.

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“…Table 1 tabulates a brief summary of major studies on developing such a system. [40] analysis and provide an indication of structural behaviour.…”

Section: Automated Integration Of Designmentioning

confidence: 99%

Towards intelligent structural design of buildings: A BIM-based solution

Hamidavi

Abrishami

Hosseini

2020

Journal of Building Engineering

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“…Singh and Gu (2012) explore five different generative design approaches and create a framework which can combine them. Use of generative design in AEC is often related to evolutionary processes and multiple solutions, which is especially desirable in the concept design phase (Hofmeyer and Davila Delgado, 2013); in the developed design phase the term 'algorithmic design' would be more suitable for generation of structural analysis models because it is characterized by an identifiable correlation between the algorithm and its outcome (Caetano et al, 2020). Zhu et al (2019) integrate BIM and GIS, mainly by converting the geometry defined using IFC building data model into shapefile geometry which is then used in GIS.…”

Section: Geometry Interpretations In Other Domainsmentioning

confidence: 99%

Interpreted open data exchange between architectural design and structural analysis models

Sibenik¹,

Kovačić²

2021

ITcon

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The heterogeneity of the architecture, engineering and construction (AEC) industry reflects on digital building models, which differ across domains and planning phases. Data exchange between architectural design and structural analysis models poses a particular challenge because of dramatically different representations of building elements. Existing software tools and standards have not been able to deal with these differences. The research on inter-domain building information modelling (BIM) frameworks does not consider the geometry interpretations for data exchange. Analysis of geometry interpretations is mostly project-specific and is seldom reflected in general data exchange frameworks. By defining a data exchange framework that engages with varying requirements and representations of architectural design and structural analysis in terms of geometry, which is open to other domains, we aim to close the identified gap. Existing classification systems in software tools and standards were reviewed in order to understand architectural design and structural analysis representations and to identify the relationships between them. Following the analysis, a novel data management framework based on classification, interpretation and automation was proposed, implemented and tested. Classification is a model specification including domain-specific terms and relationships between them. Interpretations consist of inter-domain procedures necessary to generate domain-specific models from a provided model. Automation represents the connection between open domain-specific models and proprietary models in software tools. Practical implementation with a test case demonstrated a possible realization of the proposed framework. The innovative contribution of the research is a novel framework based on the system of open domain-specific classifications and procedures for the inter-domain interpretation, which can prepare domain-specific models on central storage. The main benefit is a centrally prepared domain-specific model, relieving software developers from so-far-unsuccessful implementation of complex inter-domain interpretations in each software tool, and providing end users with control over the data exchange. Although the framework is based on the exchange between architectural design and structural analysis, the proposed central data management framework can be used for other exchange processes involving different model representations.

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“…For example, ANNs, a resurgent and prominent method for predictive analytics, has been used to aid in the design of water harvesting structures (Chandwani et al, 2016), predicting and controlling cooling loads in buildings (Venkatesan and Ramachandraiah, 2018), predicting risks for building maintenance (de Silva et al, 2013), and predicting the escalation of highway construction cost over time (Wilmot and Mei, 2005). Generative and Genetic Algorithms (GAs) have been used to predict structural designs solutions given limited spatial data (Davila Delgado et al 2013;Hofmeyer et al, 2013;. GAs have been used to analyse the static security of electric power systems (Canto dos Santos et al, 2015) and to generate optimal construction schedules (Faghihi et al, 2014).…”

Section: Predictive Analyticsmentioning

confidence: 99%

Big Data Analytics System for Costing Power Transmission Projects

Delgado

Bilal

Ajayi

et al. 2020

J. Constr. Eng. Manage.

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Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a Big Data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a Predictive Analytics and Modelling System (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75 million-point dataset of power transmission projects has been used as a case study. The proposed Big Data architecture is fit for purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the Body of Knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a Big Data architecture that can manage and process large varied datasets and seamless scalability.

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Automated design studies: Topology versus One-Step Evolutionary Structural Optimisation

Cited by 22 publications

References 53 publications

Towards intelligent structural design of buildings: A BIM-based solution

Towards intelligent structural design of buildings: A BIM-based solution

Interpreted open data exchange between architectural design and structural analysis models

Big Data Analytics System for Costing Power Transmission Projects

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