Evaluation of Curtain Grouting Efficiency by Cloud Model – Based Fuzzy Comprehensive Evaluation Method

Zhu, Yushan; Wang, Xiaoling; Deng, Shihan; Zhao, Mengqi; Ao, Xuefei

doi:10.1007/s12205-019-0519-y

Cited by 26 publications

(10 citation statements)

References 48 publications

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“…Based on the established comprehensive evaluation model of the curtain-grouting efficiency, the fuzzy evaluation matrix giving the comprehensive evaluation index E of each verification hole could be obtained by Equation (27). Equation (28) represents the fuzzy evaluation matrix of the verification hole DJ1-1: 28Thus, classification of the grouting-efficiency level of the verification hole was realized.…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…The main aim of curtain grouting is to prevent leakage and to improve the strength of the ground under the dam foundation [30]. The construction efficiency of curtain grouting is closely related to the groutability (whether the grouting material can continue to be injected) [27]. The existing research mostly describes the groutability of a rock mass using permeability.…”

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confidence: 99%

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A Comprehensive Evaluation Model for Curtain Grouting Efficiency Assessment Based on Prospect Theory and Interval-Valued Intuitionistic Fuzzy Sets Extended by Improved D Numbers

et al. 2019

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Grouting-efficiency evaluation is a key element in grouting-construction control. However, most existing grouting-efficiency evaluation models do not consider the hesitation and bounded rationality of experts and have difficulty in handling the problem of incomplete decision-making information generated by experts. Furthermore, the diversity of the evaluation indicators used can be further improved. This study conducts a comprehensive evaluation model to address these problems. An objective and reasonable fuzzy evaluation method is demonstrated through the integration of interval-valued intuitionistic fuzzy sets, prospect theory, and improved D numbers. The secondary permeability index is introduced to establish a more scientific evaluation indicator system. The proposed model is implemented in evaluating the curtain-grouting efficiency of a hydropower station, and its consistency, representativeness, and superiority are validated and analyzed.

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Section: Evaluation Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

A Comprehensive Evaluation Model for Curtain Grouting Efficiency Assessment Based on Prospect Theory and Interval-Valued Intuitionistic Fuzzy Sets Extended by Improved D Numbers

et al. 2019

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“…ANN has been the most popular (thirty-five studies) in the construction/building materials field, while ANFIS and SVM were used by five, and FL and MGGP by four studies. In terms of the sub-disciplines where the AI tools were used, the list comprises a wide range of topics, such as predicting compressive strength of concrete (Pham et al , 2019; Dutta et al , 2018; Tanyildizi, 2018; Liu and Zheng, 2019; Ghanizadeh and Rahrovan, 2019; Al-Gburi et al , 2018; Sadowski et al , 2018; Gazder et al , 2017; Reddy, 2018; Behnood and Golafshani, 2018; Nguyen et al , 2019), measuring the adhesion/cohesion force between asphalt molecules at nanoscale level (Hassan et al , 2018), estimating the shear strength from the relative density, particle size, distribution (gradation), material hardness, gradation and fineness modulus (Yang et al , 2019; Li et al , 2019), confining (normal) stress, forecasting atmospheric corrosion of metallic materials (Zhu et al , 2019) and modelling the surface roughness in the turning of hardened AISI H11 steel (Saini et al , 2012).…”

Section: Discussionmentioning

confidence: 99%

“…In the structural engineering field, ANN, ANIFS and SVR have 17, six and three applications, respectively. Examples of subtopics where AI tools were used in the structural field are automatic identification of defect in the bearing (Sun et al , 2018), predicting the stable cross-sectional geometry of alluvial channel profiles (Armaghani et al , 2018; Jung et al , 2019; Gholami et al , 2019), developing new afflux methods for arched bridge constructions (Hajihassani et al , 2019), prediction of vertical settlement in the ERP system (Bui et al , 2019), detecting beam failure and joint failure (Yavuz, 2019; Yaseen et al , 2018; Alwanas et al , 2019; Nikoo et al , 2018), predicting settlement of shallow foundations and calculating the complexity of the grouting efficiency level (Zhu et al , 2019).…”

Section: Discussionmentioning

confidence: 99%

Systematic review of application of artificial intelligence tools in architectural, engineering and construction

Momade

Durdyev

Estrella

et al. 2021

FEBE

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PurposeThis study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.Design/methodology/approachA thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.FindingsThe following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.Practical implicationsThe findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.Originality/valueThere are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

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“…Li et al 18 improved the entropy method by using the Statistical Cloud Model and established a model for calculating the weight of influencing factors of dam break consequences. Zhu et al 19 based on the Cloud model used the fuzzy comprehensive evaluation method to evaluate the curtain grouting efficiency. The above analysis methods have explored the fusion of different types of monitoring data, but most of them have simply improved the fusion algorithm.…”

Section: Introductionmentioning

confidence: 99%

Similarity analysis of dam behavior characterized by multi-monitoring points based on Cloud model

Liu

2020

International Journal of Distributed Sensor Networks

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The availability of massive amount of dam safety monitoring data can make it difficult to analyze and characterize dam behavior. This article describes the use of the Cloud model to transform quantitative monitoring data into qualitative information. Each monitoring point returning dam safety data is regarded as a cloud drop, and parameters such as the expectation, entropy, and hyper-entropy of the monitoring data are obtained through a backward cloud generator to represent the operational state of the dam. The monitoring points are then treated as vectors, and the cloud similarity is calculated using the cosine value of the angle between them. The cloud similarity coefficient is then determined to characterize the similarity of dam behavior. Experimental analysis shows that the process of identifying cloud parameters has a good effect on the discovery of abnormal monitoring values regarding dam safety and demonstrates the feasibility of characterizing the dam behavior. Clustering analysis is applied to the similarity coefficients to further achieve the hierarchical management of dam monitoring points.

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Evaluation of Curtain Grouting Efficiency by Cloud Model – Based Fuzzy Comprehensive Evaluation Method

Cited by 26 publications

References 48 publications

A Comprehensive Evaluation Model for Curtain Grouting Efficiency Assessment Based on Prospect Theory and Interval-Valued Intuitionistic Fuzzy Sets Extended by Improved D Numbers

A Comprehensive Evaluation Model for Curtain Grouting Efficiency Assessment Based on Prospect Theory and Interval-Valued Intuitionistic Fuzzy Sets Extended by Improved D Numbers

Systematic review of application of artificial intelligence tools in architectural, engineering and construction

Similarity analysis of dam behavior characterized by multi-monitoring points based on Cloud model

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