scholarly journals Application of the Bayesian Networks in Construction Engineering

2020 ◽  
Vol 30 (2) ◽  
pp. 221-233
Author(s):  
Agnieszka Leśniak ◽  
Filip Janowiec
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Risk Estimation ◽  
Decision Making Under Uncertainty ◽  
Significant Development ◽  
Construction Engineering ◽  
Network Method ◽  
Engineering Problems ◽  
Engineering Projects

AbstractCurrently, significant development of methods supporting decision making under uncertainty conditions is observed. One of such methods includes Bayesian networks used in many fields of economy and science. The paper presents the use of the Bayesian network method in civil engineering problems with particular emphasis on construction engineering projects. In addition to the existing examples of the use of the method cited, the authors’ method for the risk estimation of additional works is presented.

scholarly journals Bayesian network method for decision-making about the social sustainability of infrastructure projects

2018 ◽  
Vol 176 ◽  
pp. 521-534 ◽  
Author(s):  
Leonardo A. Sierra ◽  
Víctor Yepes ◽  
Tatiana García-Segura ◽  
Eugenio Pellicer
Keyword(s):  
Decision Making ◽  
Bayesian Network ◽  
Social Sustainability ◽  
Infrastructure Projects ◽  
The Social ◽  
Network Method

scholarly journals Determination of the Type of Heart Syndrome in Traditional Chinese Medicine with the Bayesian Network Method

2020 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Asto Buditjahjanto
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Expert Knowledge ◽  
Prior Probabilities ◽  
Network Method

The determination of a disease syndrome in the TCM is difficult enough to determine because it requires a lot of experience in observing patients' symptoms that appear in disease syndrome and their disease syndrome history. Symptoms that appear in one disease syndrome are varied and can also appear in other disease syndromes. This research limits the determination of the type of syndrome only in the heart organ. The purpose of this study is to determine the type of heart syndrome in TCM by using Bayesian Networks. Bayesian Networks is used because it has the advantage of adapting expert knowledge toward the preferences of symptoms that arise at a type of heart syndrome. The expert's preference is in the weights that act as prior probabilities that are used as the basis for calculations on the Bayesian Networks. The results showed that the Bayesian Networks can be used to determine the type of heart syndrome well. The results of trials on 7 patients yield the same diagnosis between the doctor's diagnosis and the Bayesian Networks calculation

System Reliability Evaluation Using Bayesian Networks

2013 ◽  
Vol 756-759 ◽  
pp. 2457-2461
Author(s):  
Lin Ying Liu ◽  
Qin Sun ◽  
Yao Wang
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Network Inference ◽  
Fault Tree ◽  
Reliability Evaluation ◽  
Flow Chart ◽  
Inference Algorithms ◽  
Network Method ◽  
Bayesian Network Inference

Bayesian network method for system reliability evaluation which is based on a Bayesian network that transformed from a fault tree has gotten much attention these years. After a brief introduction to the method how to transform a fault tree into a Bayesian network, the paper elaborates the Bayesian network inference algorithms. The paper focuses on the way how the inference algorithms can be applied to the practice of system reliability evaluation and designs a systematic flow chart used to evaluate system reliability in a Bayesian network way. The experiment demonstrates the feasibility of the systematic flow chart.

scholarly journals Reliability based rehabilitation of water distribution networks by means of Bayesian networks

2017 ◽  
Vol 34 (1) ◽  
pp. 163-172 ◽  
Author(s):  
Abdelaziz Lakehal ◽  
Fares Laouacheria
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Dynamic Bayesian Networks ◽  
Water Distribution Networks ◽  
Network Rehabilitation

AbstractWater plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN). The main elements of water distribution network (WDN) are: pipes and valves. The work developed in this paper focuses on a water distribution network rehabilitation in the short and long term. Priorities for rehabilitation actions were defined and the information system consolidated, as well as decision-making. The reliability data were conjugated in decision making tools on water distribution network rehabilitation in a forecasting context. As the pipes are static elements and the valves are dynamic elements, a Bayesian network (static-dynamic) has been developed, which can help to predict the failure scenario regarding water distribution. A relationship between reliability and prioritization of rehabilitation actions has been investigated. Modelling based on a Static Bayesian Network (SBN) is implemented to analyse qualitatively and quantitatively the availability of water in the different segments of the network. Dynamic Bayesian networks (DBN) are then used to assess the valves reliability as function of time, which allows management of water distribution based on water availability assessment in different segments. Before finishing the paper by giving some conclusions, a case study of a network supplying a city was presented. The results show the importance and effectiveness of the proposed Bayesian approach in the anticipatory management and for prioritizing rehabilitation of water distribution networks.

A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data

2017 ◽  
Vol 3 (1) ◽  
pp. 76 ◽  
Author(s):  
Duong Tran Duc ◽  
Pham Bao Son ◽  
Tan Hanh ◽  
Le Truong Thien
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Web Applications ◽  
Service Providers ◽  
Main Idea ◽  
Classification Methods ◽  
Cost Sensitive Learning ◽  
Network Method ◽  
Demographic Attributes ◽  
Privacy Issues

Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

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