Rockburst prediction for hard rock and deep-lying long tunnels based on the entropy weight ideal point method and geostress field inversion: a case study of the Sangzhuling Tunnel

Zhou, Hang; Chen, Shikuo; Li, Hanrui; Liu, Tong; Huanlong, Wang

doi:10.1007/s10064-021-02175-9

Cited by 28 publications

(2 citation statements)

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“…The major task of stage 3 was to produce near-optimal EEMs (also called the Pareto front) by performing multiobjective optimization process. After that, the Pareto front is ranked by the entropy weight ideal point ranking method [51], and the decision results are generated. The multi-stage decision framework is explained in detail through the following four stages.…”

Section: Methodsmentioning

confidence: 99%

“…After performing the process of multi-objective optimization, sets of EEMs (Pareto front) will be obtained, and decision-makers should select the optimal EEMs satisfying their requirements. This paper offers an efficient and effective approach, called the entropy weight ideal point ranking method [51], to help decision-makers make decisions. This ranking method sorts the Pareto front by weighting different decision objectives and then calculating the closeness degree of each solution in the Pareto front.…”

Section: Ranking the Pareto Frontmentioning

confidence: 99%

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A Multi-stage Decision Framework for Optimal Energy Efficiency Measures of Educational Buildings: A case study of Chongqing

Cui

Hong

Liu

et al. 2022

Preprint

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Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gas: especially existing buildings which do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework including a multi-objective optimization model and a ranking method is proposed to help decision-makers select optimal energy efficiency measures. The multi-objective optimization model takes into account the economic objectives and the environmental objectives, expressed as retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework is implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can identify near-optimal energy efficiency measures quickly through multi-objective optimization and can select suitable energy efficiency measures by the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building is 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction of building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption is 59.84 kWh/m2, while the lowest value is 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures.

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Section: Methodsmentioning

confidence: 99%

Section: Ranking the Pareto Frontmentioning

confidence: 99%