A non-linear case-based reasoning approach for retrieval of similar cases and selection of target credits in LEED projects

Cheng, Jack Chin Pang; Lucky, J.

doi:10.1016/j.buildenv.2015.07.019

Cited by 84 publications

(26 citation statements)

References 28 publications

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“…In Turkey, Spain, and Italy, 108 LEED-CIv3 and LEED-C and S Gold projects [12] and in Finland, Sweden, Turkey, and Spain, 133 LEED-NCv3 projects [13] were evaluated. Thus, LEED-NC is the most popular system and often issued by design teams [14].…”

Section: Introductionmentioning

confidence: 99%

LEED-CIv4 Commercial Interiors: United States (2014–2019)

Pushkar

2019

Sustainability

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This paper analyzes Leadership in Energy and Environmental Design for Interior Design and Construction: Commercial Interior (LEED-CIv4) projects, thereby presenting the first empirical evidence of these projects. The aim of the study was to reveal trends in Certified, Silver, Gold, and Platinum LEED-CIv4 US projects, certified in 2014-2019. The effect size between possible and achieved points in categories and credits was measured using Cliff's δ. The following conclusions were drawn: (i) the Integrative Process, Innovation, and Regional Priority categories showed high achievements only in Platinum projects, so it is necessary to reconsider these categories toward increasing their acceptance at all certification levels; (ii) the Location and Transportation and Indoor Environmental Quality categories were quite well-designed because most credits in these categories showed varying levels of achievement (low/medium/high/very high) at all four certification levels; and (iii) the Water Efficiency, Energy and Atmosphere, and Material and Resources categories were badly designed because most credits there similarly showed low achievements at all four certification levels, thus needing more attention toward their improvement in the next LEED-CI version.

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Section: Introductionmentioning

confidence: 99%

LEED-CIv4 Commercial Interiors: United States (2014–2019)

Pushkar

2019

Sustainability

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“…In particular, a significant number of them were conducted to support the design process and identify appropriate LEED credits for the project during the early phase, when the design changes are much higher in efficiency and less costly [3,4]. Cheng and Ma (2015) studied the relationship between LEED credits and green building technologies/sustainability design strategies to improve the effectiveness of LEED credits selection [11]. M.A.…”

Section: Background Of Researchmentioning

confidence: 99%

“…Jack C.P. Cheng's paper (2015) proposed a case-based reasoning (CBR) approach to find out the suitable case study base on the similar previously certificated green building projects and suggestions on target LEED credits [11]. F. Jalaei and A. Jrade's study (2015) (BIM) explains how this integration would assist project teams in making sustainability-related decisions [15].…”

Section: Background Of Researchmentioning

confidence: 99%

“…In addition, C.P. Cheng's (2015) study mentioned that the better judgment of target credit selection and the appropriate green design technologies largely depend on the experience of managers from previous LEED projects [11]. However, construction projects are unique, and it is often difficult to participate in similar projects, especially when the number of certified LEED projects is very limited in Vietnam at present.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Analysis: Sustainable Trends and Awarded LEED 2009 Credits in Vietnam

et al. 2020

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Established by the U.S. Green Building Council (USGBC), the Leadership in Energy and Environmental Design (LEED) became a sustainable leader of green building rating systems in American and many other countries. In Vietnam, LEED is expected as a potential solution in improving the sustainable quality of buildings for residents and solving the housing/infrastructure demand with a limit in resource consumption and minimizing negative environmental impacts. The study analyzed the awarded LEED 2009 credits by investigating the data of 36 of the total 42 LEED BC+D 2009 certified projects in Vietnam. The results of the investigation indicated the awarded credits were significantly implemented in Vietnam. These results were converted based on the summary updated on LEED version 4 of the USGBC report, to become a useful guideline for green building cost-efficiency strategies. Additionally, it also served as reference data for the Vietnamese public agency to update their green regulations based on the specific characteristics of Vietnam.

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“…To prove the effectiveness of the CFST-LSTM model, its prediction performance is compared with other traditional machine learning models and commonly seen neural networks. These contain three traditional machine learning models, including LASSO regression, Ridge regression, and support vector regression (SVR), and two commonly seen neural networks, including artificial neural network (ANN) and recurrent neural network (RNN) [43][44][45][46][47]. The parameters of these algorithms were all tuned using the same grid search process.…”

Section: Model Comparisonmentioning

confidence: 99%

Prediction of Ambient PM2.5 Concentrations Using a Correlation Filtered Spatial-Temporal Long Short-Term Memory Model

Ding

Zhang

et al. 2019

Applied Sciences

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Due to the increasingly serious air pollution problem, air quality prediction has been an important approach for air pollution control and prevention. Many prediction methods have been proposed in recent years to improve the prediction accuracy. However, most of the existing methods either did not consider the spatial relationships between monitoring stations or overlooked the strength of the correlation. Excluding the spatial correlation or including too much weak spatial inputs could influence the modeling and reduce the prediction accuracy. To overcome the limitation, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) model for air quality prediction. The model is designed based on the original LSTM model and is equipped with a spatial-temporal filter (STF) layer. This layer not only takes into account the spatial influence between stations, but also can extract highly correlated sequential data and drop weaker ones. To evaluate the proposed CFST-LSTM model, hourly PM2.5 concentration data of California are collected and preprocessed. Several experiments are conducted. The experimental results show that the CFST-LSTM model can effectively improve the prediction accuracy and has great generalization.

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A non-linear case-based reasoning approach for retrieval of similar cases and selection of target credits in LEED projects

Cited by 84 publications

References 28 publications

LEED-CIv4 Commercial Interiors: United States (2014–2019)

LEED-CIv4 Commercial Interiors: United States (2014–2019)

A Comprehensive Analysis: Sustainable Trends and Awarded LEED 2009 Credits in Vietnam

Prediction of Ambient PM2.5 Concentrations Using a Correlation Filtered Spatial-Temporal Long Short-Term Memory Model

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