The Selection of Green Building Materials Using GA-BP Hybrid Algorithm

Shi, Qian; Xu, Yilong

doi:10.1109/aici.2009.74

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…Furthermore, BPNN and the hybrid algorithm GA-BP were introduced to evaluate building materials. Compared with BPNN, the hybrid GA-BP algorithm was shown to be of better value for selecting construction materials environmentally and has greater precision (Shi and Xu 2009). D'Amico et al used ANN to simultaneously solve the energy and environmental balance along the building life cycle.…”

Section: Neural Networkmentioning

confidence: 99%

“…The authors proposed a BPNN and GA-BP hybrid algorithm to evaluate green building materials. They showed that with BPNN, the GA-BP hybrid algorithm is favourable for selecting green building materials and achieves higher accuracy (Shi and Xu 2009). Wang et al introduced a Markov chain based stochastic approach and an ANN model to project periodic energy consumption distribution for each joint energy state of building condition and temperature.…”

Section: Hybrid and Ensemble ML Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

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Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

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Section: Neural Networkmentioning

confidence: 99%

Section: Hybrid and Ensemble ML Techniquesmentioning

confidence: 99%

Advances in application of machine learning to life cycle assessment: a literature review

Ghoroghi

Rezgui

Petri

et al. 2022

Int J Life Cycle Assess

View full text Add to dashboard Cite

show abstract

“…BPNN has the advantages of approximating arbitrary nonlinear mapping, strong generalization ability, fault tolerance ability, and self-learning ability [20]. However, as an algorithm based on the steepest descent of the gradient, BPNN inevitably has the disadvantages of extended learning and training time, easily falling into local optimum, and it is difficult in determining the network structure [21].…”

Section: B the Principle Of Bpnnmentioning

confidence: 99%

A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network With Genetic Algorithm Optimization

2020

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Marine unmanned vehicle is a novel robot widely used in ocean observation, and its accurate control is of significance to their path planning. We want to find a method to predict the velocity and course of this robot, which can help us realize the accurate control of it. The paper proposed a promising type of hybrid robotic fish (HRF), which can realize two kinds of motion modes on the sea surface. Firstly, the configuration and dynamic model of the HRF were analyzed elaborately. Then, to realize accurate velocity prediction under two kinds of motion modes of HRF, the influence factors are presented in a complex marine environment. Based on the influence factors to its maneuverability, such as wind or wave parameters, a velocity prediction algorithm based on back-propagation neural network (BPNN) was introduced. However, BPNN has the disadvantages of extended learning and training time, easily falling into local optimum. Then we found that genetic algorithm (GA), which is a kind of evolutionary algorithm, is suitable for our problem. Therefore, the accuracy and efficiency of the prediction algorithm were improved by adopting the genetic algorithm to optimize the weight and threshold of BPNN. Taking the experimental data from the pool test, the back-propagation neural network with genetic algorithm (GA-BPNN) forecasting model was established. Besides, the other prediction methods were compared and evaluated under the same assessment criterion to validate the proposed forecasting model. The experimental results demonstrate that the GA-BPNN model has higher accuracy and efficiency compared with other prediction algorithms, which verifies the feasibility of the velocity prediction model for hybrid robotic fish in complex ocean environments.

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“…To improve the decision-making process, an automated or streamlined process is needed that can account for environmental aspects and other aspects such as costs [40,44]. Several authors also stressed the need to take uncertainty into account [51,53,54]. This is especially a challenge for emerging technologies and novel materials [55].…”

Section: Procedural Methodsmentioning

confidence: 99%

“…A survey by Zanghelini et al [48] found that multicriteria decision analysis is typically used in three different stages in an LCA: life cycle impact assessment (e.g., weighting), life cycle inventory, and goal and scope definition. Decisions can also be made for different systems of interest, for example, the neighbourhood level [49], building level [39,40], component level [50], and material level [51,52]. A general challenge is to balance precision with timeliness, or to know "when 'good enough' is best" as formulated by Bala et al [53].…”

Section: Procedural Methodsmentioning

confidence: 99%

From Zero Emission Buildings (ZEB) to Zero Emission Neighbourhoods (ZEN): A Mapping Review of Algorithm-Based LCA

2018

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Abstract:The building industry is responsible for approximately 40% of energy consumption and 36% of greenhouse gas emissions in the European Union (EU). The most efficient way of reducing a building's environmental impact is addressing it in the design stage. Here, design freedom is the greatest, but uncertainty is high and there is a nearly limitless number of design options. Based on experiences with zero emission buildings (ZEB) and zero emission neighbourhoods (ZEN), a mapping review has been conducted to analyse how parametric life cycle assessment (LCA) and algorithms have been used to address neighbourhoods, buildings, and construction materials. Results have identified a general gap of knowledge regarding the use of parametric LCA models for decision-support purposes, demonstrated by the substantial focus on analytical methods compared to procedural methods. Implications for the evolution from ZEB to ZEN are twofold: (i) an integrated approach with multiple tools and methods is required, and (ii) further development of algorithms in the tool are needed to address complexity, sensitivity, and uncertainty. This study is expected to foster the development of algorithmic approaches to improve the ZEB tool as a decision-support tool. Further research should address the key questions of when and how.

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The Selection of Green Building Materials Using GA-BP Hybrid Algorithm

Cited by 7 publications

References 14 publications

Advances in application of machine learning to life cycle assessment: a literature review

Advances in application of machine learning to life cycle assessment: a literature review

A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network With Genetic Algorithm Optimization

From Zero Emission Buildings (ZEB) to Zero Emission Neighbourhoods (ZEN): A Mapping Review of Algorithm-Based LCA

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