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

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

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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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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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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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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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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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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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Evapotranspiration estimation using hybrid and intelligent methods

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soft computing 2023年第14期27卷 9801-9821页

作者： Amirashayeri, Amin Behmanesh, Javad Rezaverdinejad, Vahid Attar, Nasrin Fathollahzadeh Urmia Univ Dept Water Engn POB 16511 Km Sero Rd Orumiyeh *** Iran Tabriz Univ Dept Water Engn Tabriz Iran

Accurate prediction of reference evapotranspiration (ET0) is very valuable since it directly affects the amount of agricultural water needed, the allocation of water consumption and the management of irrigation systems. This research consists of two parts;in the first part, ET0 was predicted by two machine learning models (artificial neural network (ANN)) and tree model (MT) with two empirical equations (Romanenko and Schendel). In the second part, a new preprocessing algorithm, ensemble empirical mode decomposition (EEMD), was used to improve the prediction accuracy of the MT model and eliminate time-series noises. This approach was applied to several study areas in northwest of Iran including Urmia, Mahabad, Khoy, Takab, and Sardasht stations so that daily reference evapotranspiration values were calculated based on FAO-Penman equation. The parameters of average temperature, average relative humidity, average vapor pressure and average wind speed were input parameters to the models. The results of the first part showed that the performance of MT (with RMSE: 0.58 mm/day, RSD: 0.18, NSE: 0.97) has better performance than the ANN model (with RMSE: 0.70 mm/day, RSD: 0.22, NSE: 0.95). In Urmia station, EEMD-MT enhanced the MT model about 19% reduction in RMSE. Among the investigated models, the EEMD-MT model had the highest ET0 prediction accuracy in all investigated stations. The results of the machine learning models showed a better performance than the empirical used equations. This research showed that using the new EEMD processing algorithm can improve the accuracy of hydrological forecasts and time series.

关键词： Evapotranspiration Preprocessing techniques Penman-Monteith equation soft computing methods Urmia lake basin

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soft computing approaches for photovoltaic water pumping systems: A review

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CLEANER ENGINEERING AND TECHNOLOGY 2024年 22卷

作者： Saady, Ikram Majout, Btissam Adouairi, Mohamed Said Karim, Mohammed Bossoufi, Badre Almalki, Mishari Metab Alghamdi, Thamer A. H. Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar Mahraz LIMAS Lab Fes Morocco Al Baha Univ Fac Engn Dept Elect Engn Alaqiq 657797738 Saudi Arabia Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Al Baha Univ Fac Engn Elect Engn Dept Al Baha 65779 Saudi Arabia

Water pumping systems are crucial for extracting water from deep wells. However, electricity shortages and high fuel prices significantly impact the efficiency and reliability of these systems. Therefore, renewable energy sources have gained more attention as alternatives to fossil fuels. Photovoltaic (PV) energy-based pumping systems, in particular, are becoming popular, especially in rural areas where grid connections are often unavailable. Several factors influence the performance of photovoltaic water pumping systems (PVWPS), including solar irradiance, temperature, system design, maintenance, and pumping load. To ensure optimal performance under these varying conditions, two controllers are crucial. The first controller is the Maximum Power Point Tracking (MPPT) controller, designed to maximize power extraction from the PV panels under varying environmental conditions (in particular, solar radiation and temperature). The second controller regulates the speed and torque of the induction motor (IM) which drives the pump responsible for water extraction. Therefore, to improve the performance of these controllers under different conditions. This review paper first examines widely used soft computing methods, providing a detailed description of each. These methods are then applied to both the MPPT and the IM controllers, offering valuable insights for researchers looking to develop advanced PVWPS control configurations for future applications.

关键词： Artificial intelligence Direct torque control Induction motor Maximum power point tracking Photovoltaic water pumping system PV system soft computing methods

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