An Analytical Hierarchy Process-based system to evaluate the life-cycle performance of buildings at early design stage

2020 ◽  
Vol 31 ◽  
pp. 101364 ◽  
Author(s):  
Abdullah Al-Saggaf ◽  
Hassan Nasir ◽  
Tarek Hegazy
Keyword(s):  
Life Cycle ◽  
Analytical Hierarchy Process ◽  
Cycle Performance ◽  
Design Stage ◽  
Early Design ◽  
Early Design Stage ◽  
Hierarchy Process

An intelligent system for estimating full product Life Cycle Cost at the early design stage

2008 ◽  
Vol 3 (2/3) ◽  
pp. 96 ◽  
Author(s):  
Haifeng Liu ◽  
Vivekanand Gopalkrishnan ◽  
Wee Keong Ng ◽  
Bin Song ◽  
Xiang Li
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Product Life Cycle ◽  
Intelligent System ◽  
Design Stage ◽  
Product Life ◽  
Early Design ◽  
Early Design Stage

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 A MODIFIED ANALYTICAL HIERARCHY PROCESS DESIGN EVALUATION METHODS FOR PRODUCT RECOVERY

2021 ◽  
Keyword(s):  
Analytical Hierarchy Process ◽  
Design Guidelines ◽  
Life Cycles ◽  
Product Recovery ◽  
Design Stage ◽  
Design Element ◽  
Design Strategies ◽  
Early Design Stage ◽  
Design Attributes ◽  
Hierarchy Process

The absence of existing standards for product recovery planning and the associated difficulty in prioritising the conflicting design requirements are among the main challenges faced during product design. In this paper, a concept for the Design for Multiple Life-Cycles (DFMLC) is proposed to address this situation. The objective of the DFMLC model is to assist designers in evaluating design attributes of Multiple Life-Cycle Products (MLCP) at the early design stage. The methodology adopted for the evaluation of MLCP design strategies has been based on a modified Analytical Hierarchy Process (AHP). Two mapping matrices of the design guidelines and design strategies concerning MLCP design attributes were developed for the modified AHP model. Disassemblability (> 21 %) was found to be the most important design element for MLCP followed by serviceability (> 20 %) and reassembly (> 12 %).

Integrating End-of-Life and Initial Profit Considerations in Product Life Cycle Design

2010 ◽  
Author(s):  
Yuan Zhao ◽  
Deborah Thurston
Keyword(s):  
Life Cycle ◽  
Product Design ◽  
End Of Life ◽  
Product Life Cycle ◽  
Design Stage ◽  
Design Decisions ◽  
End Of Life Decisions ◽  
Early Design ◽  
Early Design Stage ◽  
Life Decisions

Growing concerns from customers and the government about product disposal highlight the necessity of improving product take-back systems to retain the embedded values in disposed products. Progress has been made towards minimizing the cost of the disassembly process. While some progress has been made in improving end-of-life (EOL) value through decision making in the early design stage, contradictive objectives make it difficult to simultaneously optimize initial sales profits and EOL value. In this paper, a mathematical model is developed to integrate end-of-life recovery value considerations with product design decisions. The improvement of component reuse value or recycling value is achieved by linking design decisions in the early design stage with end-of-life decisions in order to maximize total product value across the span of the life cycle. A matrix based representation that can group components into several end-of-life modules with similar end-of-life decisions is also presented. The results are discussed to compare different design alternatives to understand their influence on product lifecycle value. The proposed method is illustrated through an example involving cell phone product design decisions and end-of-life strategies.

Identification of Human Errors During Early Design Stage Functional Failure Analysis

2018 ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Sign in / Sign up

Export Citation Format

Share Document