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soft computing methods for fatigue life estimation: A review of the current state and future trends

FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES 2020年第12期43卷 2763-2785页

作者： Kalayci, Can B. Karagoz, Sevcan Karakas, Ozler Pamukkale Univ Dept Ind Engn Fac Engn Denizli Turkey Pamukkale Univ Dept Mech Engn Fac Engn TR-20160 Denizli Turkey

Fatigue causes cracking or breakage in a material due to repeated loads;it causes the material to become unusable. Therefore, knowing the fatigue life of materials is crucial for the implementation of designs, economy and human life. soft computing methodologies, a subset of artificial intelligence emerging to simulate human intelligence, deal with approximate models and seek solutions to complex real-life problems relying on both computational power of machines and the high accuracy of the algorithms. In this study, soft computing methods adapted for estimating/predicting the fatigue life of engineering structures and materials are investigated. For this purpose, 95 articles published between 1995 and 2020 have been examined in detail. With this review, it is aimed to reveal the efficiency of soft computing methods and contribute to their development. Recommendations have been made to draw attention to these methodologies which are expected to be used in many areas in the future.

关键词： fatigue life estimation heuristic algorithms soft computing methods

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Measurement of environmental aspect of 3-D printing process using soft computing methods

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MEASUREMENT 2015年 75卷 210-217页

作者： Garg, A. Lam, Jasmine Siu Lee Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore

For improving the environmental performance of the manufacturing industry across the globe, 3-D printing technology should be increasingly adopted as a manufacturing procedure. It is because this technology uses the polymer PLA (Polyactic acid) as a material, which is biodegradable, and saves fuel and reduces waste when fabricating prototypes. In addition, the technology can be located near to industries and fabricates raw material itself, resulting in reduction of transport costs and carbon emission. However, due to its high production cost, 3-D printing technology is not yet being adopted globally. One way of reducing the production cost and improving environmental performance is to formulate models that can be used to operate 3-D printing technology in an efficient way. Therefore, this paper aims to deploy the soft computing methods such as genetic programming (GP), support vector regression and artificial neural network in formulating the laser power-based-open porosity models. These methods are applied on the selective laser sintering (a 3-D printing process) process data. It is found that GP evolves the best model that is able to predict open porosity satisfactorily based on given values of laser power. The laser power-based-open porosity model formulated can assist decision makers in operating the SLS process in an effective and efficient way, thus increasing its viability for being adopted as a manufacturing procedure and paving the way for a sustainable environment across the globe. (C) 2015 Published by Elsevier Ltd.

关键词： Selective laser sintering soft computing methods Open porosity prediction 3-D printing Environmental aspect

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Fatigue behaviors prediction method of welded joints based on soft computing methods

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MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2013年 559卷 574-582页

作者： Yang, Xinhua Deng, Wu Zou, Li Zhao, Huimin Liu, Jingjing Dalian Jiaotong Univ Sch Mat Sci & Engn Dalian 116028 Peoples R China Dalian Jiaotong Univ Software Inst Dalian 116052 Peoples R China

In allusion to the highly nonlinear, multivariable, strong coupling and interaction of various factors of the welded process, it is difficult to predict the fatigue behaviors of welded joints. We make use of the capabilities and advantages of rough set theory, ant colony algorithm and BP neural network, a novel fatigue behaviors prediction method (RST-ACO-BPNN) of welded joints based on RST, ACO and BPNN is proposed in this paper. The proposed RST-ACO-BPNN method utilizes the knowledge reduction ability of rough set theory for dealing with the original fatigue sample data, the minimum fatigue feature subset is obtained. The ant colony algorithm with the ability of strong global search was used to optimize the weights of BP neural network for obtaining the optimized BP neural network model. Then the minimum reduced subset was inputted into the optimized BP neural network model to construct the novel fatigue behaviors prediction model of welded joints by the continuous training and adjusting. To verify the correctness and validity of the novel RST-ACO-BPNN prediction model by applying in aluminum alloy welded joints. The simulation results show the proposed method can predict effectively the fatigue behaviors of aluminum alloy welded joints. And the RST-ACO-BPNN prediction method is provided with the merits of building model easily, simple structure, high precision and good generalization. Consequently, the prediction method can provide an effective approach to predict the fatigue behaviors of welded joints. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Rough set theory Ant colony algorithm BP neural network soft computing methods Fatigue behaviors prediction method Welded joints

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Fault detection using thermal image based on soft computing methods: Comparative study

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MICROELECTRONICS RELIABILITY 2017年 71卷 56-64页

作者： Al-Obaidy, Furat Yazdani, Farhang Mohammadi, Farah A. Ryerson Univ Dept Elect & Comp Engn 350 Victoria St Toronto ON M5B 2K3 Canada

This paper presents Integrated Circuit (IC) fault detection of a Printed Circuit Board (PCB) model using thermal image processing. The thermal image is captured and processed from the PCB model by the finite element method (FEM). The histogram features are extracted from the ICs hotspots which are used as inputs in a classifier model. The effective features are minimized by the principal component analysis method. In this work, a comparative study for image classification and detection is performed based on three soft computing techniques: multilayer perceptron, support vector machine, and adaptive neuron-fuzzy inference system. The effectiveness of the models is evaluated by comparing the performance and accuracy of the classification. To validate the model, the experimental evaluation is performed on Arduino UNO in order to detect the fault condition on the real time operating PCB. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Thermal testing soft computing methods Integrated Circuit Principal component analysis Histogram analysis

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Prediction of the dynamic pressure distribution in hydraulic structures using soft computing methods

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soft computing 2021年第5期25卷 3873-3888页

作者： Samadi, Mehrshad Sarkardeh, Hamed Jabbari, Ebrahim Iran Univ Sci & Technol IUST Sch Civil Engn POB 16765-163 Tehran Iran Hakim Sabzevari Univ Dept Civil Engn Sabzevar Khorasan Razavi Iran

Prediction of dynamic pressure distribution (P*) is a subject of great importance in the design and operation of the hydraulic structures. Flip buckets, using as hydraulic structures for dissipation of the excess energy outflow, are usually constructed at the end of the chute of the spillways. In this research, based on experimental studies of large hydraulic models, five well-known soft computing methods including artificial neural networks (ANN), gene expression programming (GEP), classification and regression trees (CART), M5 model tree (M5MT), and multivariate adaptive regression splines (MARS) approaches are examined. Mathematical expressions are obtained by these methods to predict P* in flip buckets. Compared to ANN and GEP expressions, explicit formulas derived by CART, M5MT, and MARS demonstrated more straightforward calculations. In addition, linear and nonlinear equations are generated for better comparison with the outcomes of the proposed soft computing methods. The obtained results showed the high performance of the suggested soft computing methods for the prediction of P* in flip buckets. It is found that the GEP approach culminated in more accurate results than other proposed soft computing methods and conventional linear and nonlinear regression techniques. Error measures in the testing stage showed that the formula provided by GEP with root mean square error (RMSE = 0.095), scatter index (SI = 13%), and mean absolute error (MAE = 0.073) has the best accuracy among the other predictive equations.

关键词： Dynamic pressure distribution Hydraulic structures soft computing methods

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Applications of soft computing methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2207-2238页

作者： Mojtaba Yari Manoj Khandelwal Payam Abbasi Evangelos I.Koutras Danial Jahed Armaghani Panagiotis G.Asteris Department of Mining Engineering Faculty of EngineeringMalayer UniversityMalayer***Iran Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC 3350Australia Marane Olia Ornamental Stone Mine Sanandaj***Iran Computational Mechanics Laboratory School of Pedagogical and Technological EducationAthens15122Greece School of Civil and Environmental Engineering University of Technology SydneySydneyNSW 2007Australia

Geo-engineering problems are known for their complexity and high uncertainty levels,requiring precise defini-tions,past experiences,logical reasoning,mathematical analysis,and practical insight to address them *** computing(SC)methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning *** traditional hard computing approaches,SC models use soft values and fuzzy sets to navigate uncertain *** study focuses on the application of SC methods to predict backbreak,a common issue in blasting operations within mining and civil ***,which refers to the unintended fracturing of rock beyond the desired blast perimeter,can significantly impact project timelines and *** study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations,specifically focusing on backbreak *** research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.

关键词： Backbreak blasting soft computing methods prediction theory-guided machine learning

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Bond strength prediction of spliced GFRP bars in concrete beams using soft computing methods

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COMPUTERS AND CONCRETE 2021年第4期27卷 305-317页

作者： Shahri, Saeed Farahi Mousavi, Seyed Roohollah Univ Sistan & Baluchestan Dept Civil Engn Daneshgah St Zahedan Iran

The bond between the concrete and bar is a main factor affecting the performance of the reinforced concrete (RC) members, and since the steel corrosion reduces the bond strength, studying the bond behavior of concrete and GFRP bars is quite necessary. In this research, a database including 112 concrete beam test specimens reinforced with spliced GFRP bars in the splitting failure mode has been collected and used to estimate the concrete-GFRP bar bond strength. This paper aims to accurately estimate the bond strength of spliced GFRP bars in concrete beams by applying three soft computing models including multivariate adaptive regression spline (MARS), Kriging, and M5 model tree. Since the selection of regularization parameters greatly affects the fitting of MARS, Kriging, and M5 models, the regularization parameters have been so optimized as to maximize the training data convergence coefficient. Three hybrid model coupling soft computing methods and genetic algorithm is proposed to automatically perform the trial and error process for finding appropriate modeling regularization parameters. Results have shown that proposed models have significantly increased the prediction accuracy compared to previous models. The proposed MARS, Kriging, and M5 models have improved the convergence coefficient by about 65, 63 and 49%, respectively, compared to the best previous model.

关键词： bond strength spliced GFRP bars concrete beams soft computing methods genetic algorithm

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Investigation of the Usability of soft computing methods to Determine the Quality Distribution in Metallic Mine

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JOURNAL OF MULTIPLE-VALUED LOGIC AND soft computing 2021年第5-6期37卷 493-502页

作者： Ozdemir, Ali Can Cukurova Univ Dept Min Engn TR-01330 Adana Turkey

For successful mining, the activities carried out must provide maximum profitability and be sustainable. These conditions will be ensured by performing detailed mine planning studies and applying these plans correctly in mines. Given the regional variability of mines, the quality distribution must be determined. At this stage, the modeling or estimation methods in the literature that will be used are a matter of debate. This study aimed to investigate the usability of artificial neural networks (ANNs), which are one of the soft computing methods, for determining quality distribution by estimating to the tenor of metallic mine fields. In this study, a sample application field was selected. The tenor values of this field were estimated with 71% correlation coefficient, and quality distribution was determined. The results showed that the ANN method can be an alternative to widely used modeling and estimation methods.

关键词： Metallic mine quality distribution soft computing methods tenor

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USING soft-computing methods FOR ENVIRONMENTAL ASSESSMENT 15th

USING SOFT-COMPUTING METHODS FOR ENVIRONMENTAL ASSESSMENT

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15th International Multidisciplinary Scientific Geoconference (SGEM)

作者： Tulbure, Ildiko Univ 1 December 1918 Alba Iulia Romania Tech Univ Clausthal Clausthal Zellerfeld Germany

ISBN: (纸本)9786197105414

Economic activities, especially industrial ones have the direct goal to help increasing the quality of life of the population. Beside positive direct and desired effects of industrial activities these have also negative, undesired and sometimes unthinkable effects on the environment and society. With the goal of assuring the sustainable development of our human society it is therefore necessary to evaluate economic activities not only from economic and technological points of view but from environmental and social ones as well. For this goal appropriate indicators for economic, environmental and social aspects have to be developed and used. In this context environmental indicators on local and regional level are to be used. General notions regarding environmental indicators will be presented, as well as application examples from the practice. A possibility to define in a modular way an environmental indicator will be shown. An aggregation method based on fuzzy logic will be analysed for the concrete situation of assessing the air pollution on a regional level and the advantages of using such soft-computing methods will be emphasised. The obtained results will be discussed and conclusions concerning the usage potential of the presented methodology will be drawn.

关键词： Environmental Assessment Environmental Indicators Air Pollution soft computing methods Fuzzy-Logic

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Survey of data aggregation techniques using soft computing in wireless sensor networks

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IET INFORMATION SECURITY 2013年第4期7卷 336-342页

作者： Dhasian, Hevin Rajesh Balasubramanian, Paramasivan St Xaviers Catholic Coll Engn Dept Informat Technol Alur Tamil Nadu India Natl Engn Coll Dept Comp Sci & Engn Kovilpatti Tamil Nadu India

In wireless sensor networks (WSN), data aggregation using soft computing methods is a challenging issue because of the security factors. When a node is compromised, it is easy for an adversary to inject false data and mislead the aggregator to accept false readings. Therefore there is a need for secure data aggregation. Although sufficient works on the survey of data aggregation in WSNs are done, it seems less satisfactory in terms of maintaining a secured data aggregation, and measuring accurate values. This study presents an up to date survey of major contributions to the security solutions in data aggregation which mainly use soft computing techniques. Here, classification of protocols is done according to the soft computing technique as: fuzzy logic, swarm intelligence, genetic algorithm and neural networks. Accuracy, energy consumption, cost reduction and security measures are the metrics used for the classification. Finally, the authors provide a comparative study of all aggregation techniques.

关键词： data handling telecommunication computing telecommunication security uncertainty handling wireless sensor networks data aggregation techniques wireless sensor networks WSN soft computing methods security factors data aggregation security fuzzy logic swarm intelligence genetic algorithm neural networks

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