Life cycle assessment based environmental impact estimation model for pre-stressed concrete beam bridge in the early design phase

2017 ◽  
Vol 64 ◽  
pp. 47-56 ◽  
Author(s):  
Kyong Ju Kim ◽  
Won Gun Yun ◽  
Namho Cho ◽  
Jikwang Ha
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Concrete Beam ◽  
Design Phase ◽  
Estimation Model ◽  
Early Design ◽  
Beam Bridge ◽  
Early Design Phase

scholarly journals Adaptability of Primary and Middle Schools to Post-Pandemic Reuse - A Discussion in the Context of Flexibility

2020 ◽  
pp. 5-22
Author(s):  
Orkan Zeynel Guzelci ◽  
Sema Alacam ◽  
Serkan Kocabay ◽  
Elif Isik Akkuyu
Keyword(s):  
Middle School ◽  
Life Cycle ◽  
Building Envelope ◽  
School Buildings ◽  
Design Phase ◽  
Early Design ◽  
Outdoor Space ◽  
Function Relationship ◽  
School Plans ◽  
Early Design Phase

This study discusses how the existing primary and middle school buildings can be adapted to the new needs emerging in the Covid-19 process. The three levels of adaptation are defined as follows: Building envelope-outdoor space relationship, plan layout-function relationship, and furniture relocation. In the scope of this study, five selected school plans were evaluated in the context of flexibility in the plan layout-function relationship. In this study, the concept of “adaptation” is considered as a design approach at the early design phase and/or intervention to respond to a new need in the life cycle of the building.

scholarly journals Toward LCA-lite: A Simplified Tool to Easily Apply LCA Logic at the Early Design Stage of Building in Australia

2019 ◽  
Vol 8 (5) ◽  
pp. 383 ◽  
Author(s):  
Toktam B. Tabrizi ◽  
Arianna Brambilla
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Environmental Impact ◽  
Environmental Performance ◽  
Building Design ◽  
Design Stage ◽  
Effective Parameters ◽  
Early Design ◽  
Early Design Stage ◽  
Knowledge Based

Life Cycle Assessment (LCA), developed over 30 years ago, has been helpful in addressing a growing concern about the direct and indirect environmental impact of buildings over their lifetime. However, lack of reliable, available, comparable and consistent information on the life cycle environmental performance of buildings makes it very difficult for architects and engineers to apply this method in the early stages of building design when the most important decisions in relation to a building’s environmental impact are made. The LCA quantification method with need of employing complex tools and an enormous amount of data is unfeasible for small or individual building projects. This study discusses the possibility of the development of a tool that allows building designers to more easily apply the logic of LCA at the early design stage. Minimising data requirements and identifying the most effective parameters that promise to make the most difference, are the key points of simplification method. The conventional LCA framework and knowledge-based system are employed through the simplification process. Results of previous LCA studies in Australia are used as the specific knowledge that enable the system to generate outputs based on the user’s inputs.Keywords: Life Cycle Assessment (LCA), early design stage, most effective parameters, life cycle environmental performance

scholarly journals Early Design Space Exploration with Model-Based System Engineering and Set-Based Design

Systems ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 45 ◽  
Author(s):  
Eric Specking ◽  
Gregory Parnell ◽  
Edward Pohl ◽  
Randy Buchanan
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Design Space ◽  
Design Phase ◽  
Analysis Framework ◽  
Trade Off ◽  
Early Design ◽  
Integrated Framework ◽  
Set Based Design ◽  
Early Design Phase

Adequately exploring the tradespace in the early system design phase is important to determine the best design concepts to pursue in the next life cycle stage. Tradespace exploration (TSE) often uses trade-off analysis. Set-based design (SBD) methods, compared to traditional point-based design, explore significantly more designs. An integrated framework with model-based system engineering (MBSE) and a life cycle cost model enables design evaluation in near real-time. This study proposes an early design phase SBD methodology and demonstrates how SBD enabled by an integrated framework with MBSE and life cycle cost provides an enhanced TSE that can inform system design requirements and help decision makers select high performing designs at an affordable cost. Specifically, this paper (1) provides an overview of TSE and SBD, (2) describes the Integrated Trade-off Analysis Framework, (3) describes a methodology to implement SBD in the early design phase, and (4) demonstrates the techniques using an unmanned aerial vehicle case study. We found that the Integrated Trade-off Analysis Framework informs requirement development based upon how the requirements affect the feasible tradespace. Additionally, the integrated framework that uses SBD better explores the design space compared to traditional methods by finding a larger set of feasible designs early in the design process.

Quantifying the Impact of Sustainable Product Design Decisions in the Early Design Phase Through Machine Learning

2016 ◽  
Author(s):  
Addison Wisthoff ◽  
Vincenzo Ferrero ◽  
Tony Huynh ◽  
Bryony DuPont
Keyword(s):  
Environmental Impact ◽  
Product Design ◽  
Sustainable Design ◽  
Design Phase ◽  
Product Attributes ◽  
Design Decisions ◽  
Early Design ◽  
The Impact ◽  
The Relationship ◽  
Early Design Phase

As more companies and researchers become interested in understanding the relationship between product design decisions and eventual environmental impact, proposed methods have explored meeting this demand. However, there are currently limited methods available for use in the early design phase to help quantify the environmental impact of making design decisions. Current methods, primarily vetted Life Cycle Assessment (LCA) methods, require the designer to wait until later in the design phase, when a product’s design is more defined; alternatively, designers are resigned to relying on prior sustainable design experience and empirical knowledge. There is a clear need to develop methods that quantitatively inform designers of the environmental impact of design decisions during the early design phase (particularly during concept generation), as this allows for reexamination of decisions before they become costly or time-intensive to change. The current work builds on previous research involving the development of a search tree of sustainable design knowledge, which, applied during the early design phase, helps designers hone in on the impact of product design decisions. To assist in quantifying the impact of these design decisions, the current work explores the development of a weighting system associated with each potential design decision. The work presented in this paper aims to quantify the general environmental impact potential design decisions have on a consumer product, by using a multi-layer perceptron neural network with back propagation training — a method of machine learning — to relate the life-cycle assessment impact of 37 case study products to product attributes. By defining the relationship between LCA data and product attributes, designers in the early design phase will be more informed of which product attributes have the largest environmental impact, such that the designer can redesign the product to have reduce this impact.

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