scholarly journals Preliminary Service Life Estimation Model for MEP Components Using Case-Based Reasoning and Genetic Algorithm

2019 ◽  
Vol 11 (11) ◽  
pp. 3074 ◽  
Author(s):  
Nahyun Kwon ◽  
Kwonsik Song ◽  
Moonseo Park ◽  
Youjin Jang ◽  
Inseok Yoon ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Service Life ◽  
Absolute Error ◽  
Case Based Reasoning ◽  
Estimation Model ◽  
Sustainable Living ◽  
Building Maintenance ◽  
Building Components ◽  
Proactive Measures ◽  
Case Based

In recent decades, building maintenance has been recognized as an important issue as the number of deteriorating buildings increases around the world. In densely populated cities, building maintenance is essential for ensuring sustainable living and safety for residents. Improper maintenance can not only cause enormous maintenance costs, but also negatively affect residents and their environment. As a first step, the service life of building components needs to be estimated in advance. Mechanical, electrical, and plumbing (MEP) components especially produce many maintenance-related problems compared to other components. In this research, a model was developed that applies the genetic algorithm (GA) and case-based reasoning (CBR) methodologies to estimating the service life of MEP components. The applicability of the model was tested by comparing the outputs of 20 randomly selected test cases with those of retrieved similar cases. The experimental results demonstrated that the overall similarity scores of the retrieved cases were over 90%, and the mean absolute error rate (MAER) of 10-NN was approximately 7.48%. This research contributes to the literature for maintenance management by not only presenting an approach to estimating the service life of building components, but also by helping convert the existing maintenance paradigm from reactive to proactive measures.

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

2019 ◽  
Vol 11 (24) ◽  
pp. 7181
Author(s):  
Sojin Park ◽  
Nahyun Kwon ◽  
Yonghan Ahn
Keyword(s):  
Fuzzy Ahp ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Average Case ◽  
Related Information ◽  
Building Maintenance ◽  
Proposed Model ◽  
Building Components ◽  
Hierarchy Process ◽  
Case Based

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.

scholarly journals Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

2015 ◽  
Vol 7 (4) ◽  
pp. 4318-4342 ◽  
Author(s):  
Jie Dou ◽  
Kuan-Tsung Chang ◽  
Shuisen Chen ◽  
Ali Yunus ◽  
Jin-King Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Image Analysis ◽  
Object Oriented ◽  
Case Based Reasoning ◽  
Case Based

scholarly journals Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

2019 ◽  
Vol 11 (3) ◽  
pp. 871 ◽  
Author(s):  
Nahyun Kwon ◽  
Joosung Lee ◽  
Moonsun Park ◽  
Inseok Yoon ◽  
Yonghan Ahn
Keyword(s):  
Absolute Error ◽  
Distance Measurement ◽  
Error Rates ◽  
Measurement Methods ◽  
Case Based Reasoning ◽  
Noise Prediction ◽  
Cost Overruns ◽  
Construction Firms ◽  
Sustainable Environment ◽  
Case Based

Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

scholarly journals A Case-Based Reasoning Model for Retrieving Window Replacement Costs through Industry Foundation Class

2019 ◽  
Vol 9 (22) ◽  
pp. 4728 ◽  
Author(s):  
Hyunjoo Kim ◽  
Jonghyeob Kim
Keyword(s):  
Building Information Modeling ◽  
Cost Estimate ◽  
Information Modeling ◽  
Case Based Reasoning ◽  
Large Database ◽  
Cost Information ◽  
Related Data ◽  
Building Information ◽  
Building Components ◽  
Case Based

Building information modeling (BIM) provides facility managers with a large database consisting of 3D geometric data as well as management data. In particular, Industry Foundation Class (IFC) has been applied in many studies as it provides extensive and diverse information regarding building components. With the use of BIM combined with case-based reasoning (CBR), in this study, a model was developed to estimate replacement costs by retrieving cost information from IFC. This study focused on the replacement of windows for office buildings, and the costs associated with that replacement. Two main advantages were identified in the proposed approach. First, the replacement information required for the comparison of different cases is automatically obtained from a BIM file and parsed for predicting a cost estimate using IFC. Next, the accuracy is increased by matching various cost-related data such as contractors and manufacturers in the estimation of replacement costs with the help of CBR.

DEFECT PREDICTION USING CASE-BASED REASONING: AN ATTRIBUTE WEIGHTING TECHNIQUE BASED UPON SENSITIVITY ANALYSIS IN NEURAL NETWORKS

2012 ◽  
Vol 22 (06) ◽  
pp. 747-768 ◽  
Author(s):  
ELHAM PAIKARI ◽  
MICHAEL M. RICHTER ◽  
GUENTHER RUHE
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Absolute Error ◽  
Defect Prediction ◽  
Case Based Reasoning ◽  
Data Sets ◽  
Attribute Weighting ◽  
Weight Calculation ◽  
The Impact ◽  
Case Based

Software defect prediction is an acknowledged approach used to achieve better product quality and to better utilize resources needed for that purpose. One known method for predicting the number of defects is to apply case-based reasoning (CBR). In this paper, different attribute weighting techniques for CBR-based defect prediction are analyzed. One of the weighting techniques used in this work, Sensitivity Analysis based on Neural Networks (SANN), is based on sensitivity analysis of the impact of attributes as part of neural network analysis. Neural networks are applicable when there are non-linear and complicated relationships among the attributes. Since weighting plays a key role in the CBR model, using an efficient weight calculation method can change the results. The results of SANN are compared with applying uniform weights and weights gained from Multiple Linear Regression (MLR).Evaluation of the accuracy of the overall method for applying the three different weighting techniques is done over five data sets, comprising about 5000 modules from NASA. Two quality measures are applied: Average Absolute Error (AAE) and Average Relative Error (ARE). In addition to the variation of weighting techniques, the impact of varying the number of nearest neighbors is studied.The three main results of the empirical analysis are: (i) In the majority of cases, SANN achieves the most accurate results; (ii) uniform weighting performs better than the MLR-based weighting heuristic; and (iii) there is no significant preference pattern for defining the number of similar objects used for prediction in CBR.

Case-based reasoning and BIM systems for asset management

2015 ◽  
Vol 5 (3) ◽  
pp. 233-247 ◽  
Author(s):  
Ibrahim Motawa ◽  
Abdulkareem Almarshad
Keyword(s):  
Asset Management ◽  
Information Exchange ◽  
Relevant Information ◽  
Integrated System ◽  
Case Based Reasoning ◽  
Content Type ◽  
Building Maintenance ◽  
Building Models ◽  
Building Information ◽  
Case Based

Purpose – The next generation of Building Information Modelling (BIM) seeks to establish the concept of Building Knowledge Modelling (BKM). The current BIM applications in construction, including those for asset management, have been mainly used to ensure consistent information exchange among the stakeholders. However, BKM needs to utilise knowledge management (KM) techniques into building models to advance the use of these systems. The purpose of this paper is to develop an integrated system to capture, retrieve, and manage information/knowledge for one of the key operations of asset management: building maintenance (BM). Design/methodology/approach – The proposed system consists of two modules; BIM module to capture relevant information and case-based reasoning (CBR) module to capture the operational knowledge of maintenance activities. The structure of the CBR module was based on analysis of a number of interviews and case studies conducted with professionals working in public BM departments. This paper discusses the development of the CBR module and its integration with the BIM module. The case retaining function of the developed system identifies the information/knowledge relevant to maintenance cases and pursues the related affected building elements by these cases. Findings – The paper concludes that CBR as a tool for KM can improve the performance of BIM models. Originality/value – As the research in BKM is still relatively immature, this research takes an advanced step by incorporating the intelligent functions of knowledge systems into BIM-based systems which helps the transformation from the conventional BIM to BKM.

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