Maintenance cost prediction for aging residential buildings based on case-based reasoning and genetic algorithm

Kwon, Nahyun; Song, Kwonsik; Ahn, Yonghan; Park, Moonsun; Jang, Youjin

doi:10.1016/j.jobe.2019.101006

Cited by 59 publications

(45 citation statements)

References 39 publications

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“…Many studies have contributed to the representation and similarity mapping of engineering projects, such as the characteristic tree method [59] based on meta-synthesis and the geometric feature matching technique based on BIM models [60]. For the case automatic revision mechanism, the comparatively mature method is to adopt heuristic algorithms [61,62] by optimizing the retrieved source case scheme to obtain a more suitable scheme for the target case.…”

Section: G Reasoning Technologymentioning

confidence: 99%

Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

2020

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The Fourth Industrial Revolution (Industry 4.0) is reshaping the construction industry and bringing it into an intelligent construction era. Emerging technologies, such as the Building Information Modelling, Internet of Things, big data, cloud computing, and artificial intelligence, have penetrated into all stages of the building life cycle and play a significant role. However, the major issue of intelligent construction is integrating multiple technologies to create more potential opportunities rather than their fragmented application. Considering the various special characteristics of the construction industry and the high heterogeneity of these technologies, their integration in the construction industry is challenging and requires in-depth investigations. This paper summarizes the Industry 4.0-related technologies involved in the construction industry based on an analysis of the characteristics of the industry. Further, this study presents a framework of a cyber-physical system to integrate these technologies and improve the overall capabilities of construction organization and management. A case study of the Xiong'an citizen service center is introduced to verify the technological feasibility and preliminary implementation effect of the proposed framework. As forward-looking research, the significance of this paper may also to inspire more efforts in this field.

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Section: G Reasoning Technologymentioning

confidence: 99%

Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

2020

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“…In the study of urban typologies [22], block and street types were studied as a context-sensitive sample of types by describing the cases with semantic and geometric values, such as land use, length, area, and ground space index (GSI). Nahyun developed a model adopting case-based reasoning and a genetic algorithm to predict the maintenance costs for aging residential buildings [8].…”

Section: Urban Morphological Quantificationmentioning

confidence: 99%

“…Therefore, urban morphology provides a valuable basis for urban planners and managers. Urban morphology is related to complex urban system analysis, such as energy performance [7], citizen behavior, and economic benefits [8].…”

Section: Introductionmentioning

confidence: 99%

Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches

Cai

Guo²,

Zhang

et al. 2021

Sustainability

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The study of urban morphology contributes to the evolution of cities and sustainable development. Urban morphological feature extraction and similarity analysis represents a practical framework in many studies to interpret and introduce the current built environment to aid in proposing novel designs. In conventional methods, morphological features are represented based on qualitative descriptions, symbolical interpretation, or manually selected indicators. However, these methods could cause subjective bias and limit the generalizability. This study proposes a hybrid data-driven approach to support quantitative morphological descriptions and multi-dimensional similarity analysis for urban design decision-making and to further morphology-related studies using information abundance via a deep-learning approach. We constructed a dataset of 3817 residential plots with geometrical and related infrastructure information. A deep convolutional neural network, GoogLeNet, was implemented with the plots’ figure–ground images, by quantifying the morphological features into 2048-dimensional feature vectors. We conducted a similarity analysis of the plots by calculating the Euclidean distance between the high-dimensional feature vectors. Then, a comparison study was performed by retrieving cases based on the plot shape and plots with buildings separately. The proposed method considers the overall characteristics of the urban morphology and social infrastructure situations for similarity analysis. This method is flexible and effective. The proposed framework indicates the feasibility and potential of integrating task-oriented information to introduce custom and adequate references via deep learning methods, which could support decision making and association studies on morphology with urban consequences. This work could serve as a basis for further typo-morphology studies and other morphology-related ecological, social, and economic studies for sustainable built environments.

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“…where SIM(x a , x b ) refers to the similarity score between cases x a , x b and DIS(x a , x b ) refers to the relative distance between cases [1,55]. In addition, n means the total number of attributes and a i (x a ) is the value of the ith attribute of case x a , which is normalized by Equation (1).…”

Section: Case Retrieval Based On Cbrmentioning

confidence: 99%

Forecasting Repair Schedule for Building Components Based on Case-Based Reasoning and Fuzzy-AHP

2019

Self Cite

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Building maintenance is closely related to the performance and sustainability of buildings. However, existing approaches to maintenance are limited in terms of estimating required repairs. Therefore, this study proposes a case-based reasoning (CBR)-based model for estimating the time when the first repair will be needed after the completion of construction, even in phases where maintenance-related information is scarce. CBR and fuzzy-analytic hierarchy process (AHP) were employed as research methodologies. A database was established by collecting 257 cases related to maintenance of apartment buildings, and attributes were extracted through literature reviews and expert interviews. Then, attributes were weighted by fuzzy-AHP and case similarities were computed by measuring the Euclidean distance. Similar cases were retrieved based on similarity scores. The model was validated via a comparison of 20 randomly selected test cases with the output of retrieved cases. The results showed that the average case similarities of 3-, 5-, 7-, and 10-nearest neighbors (NN) were 98.05%, 97.86%, 97.73%, and 97.59%, respectively, and mean absolute percentage errors for 3-, 5-, 7-, and 10-NN were mostly lower than 20%, confirming the applicability of the proposed model. The proposed method will help in the preliminary estimation of the repair time of building components.

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Maintenance cost prediction for aging residential buildings based on case-based reasoning and genetic algorithm

Cited by 59 publications

References 39 publications

Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System

Urban Morphological Feature Extraction and Multi-Dimensional Similarity Analysis Based on Deep Learning Approaches

Forecasting Repair Schedule for Building Components Based on Case-Based Reasoning and Fuzzy-AHP

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