scholarly journals Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yan Liu ◽  
Ting-Hua Yi ◽  
Zhen-Jun Xu
Keyword(s):  
Early Warning ◽  
Construction Projects ◽  
Highway Construction ◽  
Early Warning Systems ◽  
Construction Safety ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Ability To Act ◽  
Case Based ◽  
Early Warnings

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods’ effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

scholarly journals Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

2020 ◽  
Vol 12 (13) ◽  
pp. 5231 ◽  
Author(s):  
Sahand Somi ◽  
Nima Gerami Seresht ◽  
Aminah Robinson Fayek
Keyword(s):  
Renewable Energy ◽  
Construction Projects ◽  
Wind Farm ◽  
Risk Identification ◽  
Case Based Reasoning ◽  
Onshore Wind ◽  
Energy Projects ◽  
Project Objectives ◽  
Tools And Techniques ◽  
Case Based

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase.

scholarly journals Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

2019 ◽  
Vol 16 (9) ◽  
pp. 1511 ◽  
Author(s):  
Yikun Su ◽  
Shijing Yang ◽  
Kangning Liu ◽  
Kaicheng Hua ◽  
Qi Yao
Keyword(s):  
Decision Making ◽  
Safety Management ◽  
Construction Safety ◽  
Case Based Reasoning ◽  
Prevention Measures ◽  
Proposed Model ◽  
Unsafe Behavior ◽  
Safety Accident ◽  
Construction Safety Management ◽  
Case Based

Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people’s unsafe behavior and objective’s unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet’s value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.

scholarly journals The epidemic volatility index, a novel early warning tool for identifying new waves in an epidemic

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Polychronis Kostoulas ◽  
Eletherios Meletis ◽  
Konstantinos Pateras ◽  
Paolo Eusebi ◽  
Theodoros Kostoulas ◽  
...  
Keyword(s):  
New York ◽  
Early Warning ◽  
New York State ◽  
Early Warning Systems ◽  
The United States ◽  
Economic Effects ◽  
Volatility Index ◽  
Ability To Act ◽  
Threshold Data ◽  
Stable Performance

AbstractEarly warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.

scholarly journals Fault Detection in the MSW Incineration Process Using Stochastic Configuration Networks and Case-Based Reasoning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7356
Author(s):  
Chenxi Ding ◽  
Aijun Yan
Keyword(s):  
Fault Detection ◽  
Back Propagation ◽  
Waste Incineration ◽  
Training Sample ◽  
Support Vector ◽  
Case Based Reasoning ◽  
Detection Model ◽  
Msw Incineration ◽  
Incineration Process ◽  
Case Based

Fault detection in the waste incineration process depends on high-temperature image observation and the experience of field maintenance personnel, which is inefficient and can easily cause misjudgment of the fault. In this paper, a fault detection method is proposed by combining stochastic configuration networks (SCNs) and case-based reasoning (CBR). First, a learning pseudo metric method based on SCNs (SCN-LPM) is proposed by training SCN learning models using a training sample set and defined pseudo-metric criteria. Then, the SCN-LPM method is used for the case retrieval stage in CBR to construct the fault detection model based on SCN-CBR, and the structure, algorithmic implementation, and algorithmic steps are given. Finally, the performance is tested using historical data of the MSW incineration process, and the proposed method is compared with typical classification methods, such as a Back Propagation (BP) neural network, a support vector machine, and so on. The results show that this method can effectively improve the accuracy of fault detection and reduce the time complexity of the task and maintain a certain application value.

scholarly journals Effective real-time forecasting of inundation maps for early warning systems during typhoons

2018 ◽  
Vol 147 ◽  
pp. 03014
Author(s):  
Jhih-Huang Wang ◽  
Gwo-Fong Lin ◽  
Bing-Chen Jhong
Keyword(s):  
Early Warning ◽  
Lead Time ◽  
Early Warning Systems ◽  
Reference Points ◽  
Support Vector ◽  
Spatial Expansion ◽  
Warning Systems ◽  
Inundation Maps ◽  
Inundation Depth ◽  
Proposed Model

Accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation during typhoons. In this paper, an effective forecasting model is proposed to yield 1- to 6-h lead-time inundation maps for early warning systems during typhoons. The proposed model based on Support Vector Machine (SVM) is composed of two modules, point forecasting and spatial expansion. In the first module, the rainfall intensity, inundation depth, cumulative rainfall and forecasted inundation depths are considered as model input for point forecasting. In the second module, the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps for spatial expansion. The results show that the proposed model is able to provide accurate point forecasts at each inundation point. Moreover, the spatial expansion module is capable of producing accurate spatial inundation forecasts. Obviously, the proposed model provides reasonable spatial inundation forecasts, and is able to deal with the nonlinear relationships between inputs and desired output. In conclusion, the proposed model is suitable and useful for inundation forecasting.

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