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Construction of graphene based photocatalysts for photocatalytic degradation of organic pollutant and modeling using artificial intelligence techniques

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PHYSICA B-CONDENSED MATTER 2021年 608卷

作者： Tabatabai-Yazdi, Fatemeh-Sadat Pirbazari, Azadeh Ebrahimian Saraei, Fatemeh Esmaeili Khalil Gilani, Neda Univ Tehran Fouman Fac Engn Hybrid Nanomat & Environm Lab Coll Engn POB 43515-1155 Fouman *** Iran Univ Tehran Fouman Fac Engn Data Min Res Grp Coll Engn POB 43515-1155 Fouman *** Iran Univ Guilan Dept Chem Engn Rasht *** Iran

In this study, graphene based photocatalysts containing different silver doped titanium dioxide nanosheets (AgTNs-RGO) were fabricated. The successful reduction of graphene oxide (as support) was confirmed by Raman analysis. Also, powder X-ray diffraction (PXRD) analysis confirmed pure anatase phase of the obtained TiO2 nanosheets as well as Raman spectroscopy. The efficiency of Ag-TNs-RGO (0.038) for degradation of tetracycline (TC) antibiotic as an organic pollutant model under visible light and UV irradiation for 180 min was evaluated 48.57% and 77.84%, respectively. artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to describe the photocatalytic degradation process. Five degradation variables (catalyst dosage, initial concentration of TC, silver nitrate content, visible light irradiation time and ammonium persulfate (APS) concentration) were defined as the input variables of the models and the removal percentage of TC was the output. Sensitivity analysis was performed by weights method on the outputs of models.

关键词： Graphene oxide Titanium dioxide nanosheets Silver Photocatalytic degradation artificial intelligence techniques

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Dynamic performance using artificial intelligence techniques and educational assessment of nanocomposite structures

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STEEL AND COMPOSITE STRUCTURES 2024年第1期53卷 115-121页

作者： Zengxia, Han Nasihatgozar, M. Shen, X. Xinzhou Teachers Univ Dept Educ Xinzhou Peoples R China Islamic Azad Univ Dept Mech Engn Kashan Branch Kashan Iran Ristab Co Dept Mech Engn Dubai U Arab Emirates

The present paper deals with a comprehensive study about dynamic performance and educational economic assessment of nanocomposite structures, while it focuses on truncated conical shells. Advanced structure dynamic behavior has been analyzed by means of AI techniques, which allow one to predict and optimize their performances with good accuracy for different loading and environmental conditions. The incorporation of the AI method significantly enhances the computational efficiency and is a powerful tool in designing nanocomposites and for their structural analysis. Further, an educational assessment is provided in the context of cost and practicality related to such structures in engineering education. This study showcases the capabilities of AI-enabled methods with regard to cost reduction, improvement of structural efficiency, and enhancement of learning engagement for students through certain practical examples on state-of-the-art nanocomposite technology. The results also confirm a remarkable capability of artificial intelligence regarding the optimization of both dynamic and economic aspects, which could be highly valued for further development of nanocomposite structures.

关键词： artificial intelligence techniques dynamic performance educational nanocomposite structures

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Predicting the compressive strength of self‑compacting concrete using artificial intelligence techniques:a review

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AI in Civil Engineering 2024年第1期3卷 178-191页

作者： Sesugh Terlumun M.E.Onyia F.O.Okafor Department of Civil and Environmental Engineering Air Force Institute of TechnologyKadunaNigeria Department of Civil Engineering University of Nigeria NsukkaNsukkaNigeria

Concrete is one of the most common construction materials used all over the *** the strength properties of concrete traditionally demands extensive laboratory ***,researchers have increasingly turned to predictive models to streamline this *** review focuses on predicting the compressive strength of self-compacting concrete using artificial intelligence(AI)***-compacting concrete represents an advanced construction material particularly suited for scenarios where traditional vibrational methods face limitations due to intricate formwork or reinforcement *** review evaluates various AI techniques through a comparative performance *** findings highlight that employing Deep Neural Network models with multiple hidden layers significantly enhances predictive ***,artificial neural network(ANN)models exhibit robustness,consistently achieving R^(2) values exceeding 0.7 across reviewed studies,thereby demonstrating their efficacy in predicting concrete compressive *** integration of ANN models is recommended for formulating various civil engineering properties requiring predictive ***,the adoption of AI models reduces both time and resource expenditures by obviating the need for extensive experimental testing,which can otherwise delay construction activities.

关键词： Prediction Compression strength Self-compacting concrete artificial intelligence techniques Review

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Prediction of friction capacity of driven piles in clay using artificial intelligence techniques

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INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING 2016年第5期10卷 469-475页

作者： Suman, Shakti Das, Sarat Kumar Mohanty, Ranajeet Natl Inst Technol Dept Civil Engn Rourkela 769008 Odisha India

Driven piles are widely used due to their easy installation set up and are economical. In this paper, prediction models for predicting friction capacity of piles in clay soils based on experimental test results are developed using two recently developed artificial intelligence techniques;multivariate adaptive regression splines (MARS) and functional networks (FN). The efficacy of developed MARS and FN models has been compared with previously developed models as given in the literature in terms of statistical parameters like correlation coefficient (R), Nash-Sutcliffe coefficient of efficiency (E), absolute average error, maximum average error, root mean square error and normalised mean bias error. Based on statistical performances, MARS and FN models are found to have a better predictive capacity than existing models.

关键词： Pile Clay Friction capacity artificial intelligence techniques Multivariate adaptive regression splines Functional network Statistical performance criteria

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Comparative analysis of artificial intelligence techniques for the prediction of infiltration process

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Geology, Ecology, and Landscapes 2021年第2期5卷 109-118页

作者： Singh, Balraj Sihag, Parveen Parsaie, Abbas Angelaki, Anastasia Civil Engineering Department Panipat Institute of Engineering and Technology Panipat India Civil Engineering Department Shoolini University Solan India Hydro-Structure Engineering Shahid Chamran University of Ahvaz Ahvaz Iran Department of Agriculture Crop Production and Rural Environment University of Thessaly Volos Greece

Knowledge of the infiltration process is beneficial in designing and planning of irrigation networks, soil erosion, hydrologic design, and watershed management. In this study, the infiltration process was analyzed using predictive models of artificial neural network (ANN), multi-linear regression (MLR), Random Forest regression (RF), M5P tree, and their performances were compared with the empirical model: Kostiakov model. Field experimental data was implemented for training and testing the above models, and their outcomes were assessed with the help of suitable performance assessment parameters. These models were assessed using a field dataset containing 340 observations, out of which 70% were used for the training purpose and the remaining for the testing. The RF-based models perform better than other models with Nash-Sutcliffe model efficiency (NSE) equal to 0.9963 and 0.9904 for the training and testing stages, correspondingly. ANN, MLR, and M5P model also give a good prediction performance, but the Kostiakov model’s performance is inferior. Sensitivity investigation suggests that the parameters, cumulative time, and moisture content in the soil are the most influential parameters for assessing the cumulative infiltration of soil. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).

关键词： artificial intelligence techniques Infiltration process kostiakov model multi-linear regression nash-sutcliff efficiency

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Transient settlement estimation of shallow foundation under eccentrically inclined static arid cyclic load on granular soil using artificial intelligence techniques

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GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL 2023年第6期18卷 560-576页

作者： Sasmal, Suvendu Kumar Behera, Rabi Narayan Natl Inst Technol Dept Civil Engn Rourkela India

The present study focuses on estimating the transient response of shallow strip footing on granular soil using soft computing techniques. A shallow foundation is numerically modelled using Beam on Nonlinear Winkler Foundation model. Then the footing is subjected to a combination of allowable static and cyclic load depending on the ultimate bearing capacity of the footing. The eccentricity and angle of load inclination of static load are varied to simulate more practical conditions. The cyclic load is rectangular pulse load. One cycle of rectangular pulse load is applied to observe the immediate response of the foundation, referred as the transient response. Apart from the loading parameters, three granular soils of three different relative densities (D,= 35%, 51% and 69%) are considered. Based on numerical simulation of 1728 conditions, soft computing models are developed using five techniques, viz. Neural Networks, Support Vector Machines, Multivariate Adaptive Regression Splines, Adaptive Neuro Fuzzy Interface System and Multi Gene Genetic Programming. It is found that the static load on the foundation is the most important parameter controlling the transient response of the footing.

关键词： Shallow foundation transient response rectangular pulse load load eccentricity load inclination artificial intelligence techniques

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Prediction of vertical pile capacity of driven pile in cohesionless soil using artificial intelligence techniques

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INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING 2018年第2期12卷 209-216页

作者： Mohanty, Ranajeet Suman, Shakti Das, Sarat Kumar Natl Inst Technol Dept Civil Engn Rourkela 769008 Odisha India

Piles are used in substructures of different infrastructural constructions. Due to the complex nature of soil, there are different empirical models to predict the bearing capacity of piles. The objective of the present study is to develop prediction models for vertical loaded driven piles in cohesionless soil using a novel artificial intelligence (AI) technique multi-objective genetic programming (MOGP). Two other recent AI techniques, multivariate adaptive regression spline (MARS) and functional network (FN), are also used to compare the efficacy of different AI techniques. The results MOGP, MARS and FN models are compared in terms of different statistical parameters such as correlation coefficient (R), absolute average error, root-mean-square-error, overfitting ratio and P-50. A ranking criteria approach has been implemented to assess the performance of the prediction models developed in this study along with other AI and empirical models available in the literature. The predictive model equations based on MOGP, MARS and FN are also presented.

关键词： Pile Vertical pile capacity artificial intelligence techniques Multivariate adaptive regression splines Functional network Multi-objective genetic programming Statistical performance

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Application of artificial intelligence techniques to the Study of Machine Signatures

Application of Artificial Intelligence Techniques to the Stu...

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International Conference on Electrical Machines

作者： W.-Y. Chen J.-X. Xu S.K Panda Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117576

ISBN: (纸本)9781467301435

this paper presents demonstration on the application of artificial intelligence techniques to the study of machine vibration signatures. First, a Self-Organizing Map (SOM) is used to discover cluster information from frequency-domain vibration signatures for the detection and diagnosis of unbalanced rotor and bearing faults. In the next, with further feature extraction in frequency-domain, a 2-dimensional multi- class Support Vector Machine (SVM) is used to predict these fault modes with an error rate of 1.48% over a wide machine operation speed.

关键词： artificial intelligence techniques Study Machine Signatures

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Rockburst prediction using artificial intelligence techniques:A review

岩石力学通报（英文）

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岩石力学通报（英文） 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction artificial intelligence techniques

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artificial intelligence Versus Conventional Mathematical techniques: A Review for Optimal Placement of Phasor Measurement Units

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TECHNOLOGY AND ECONOMICS OF SMART GRIDS AND SUSTAINABLE ENERGY 2016年第1期1卷 1-13页

作者： Negash, Kinfe Khan, Baseem Yohannes, Estifanos Hawassa Univ Inst Technol Hawassa Ethiopia

The Phasor Measurement Unit (PMU) is a contemporary metering device installed on system to enable the power system monitoring and control. PMUs are most accurate and advance time synchronized technology which provides measurements of voltages at the buses and also current phase values which are connected to those buses where these PMUs are located. PMU placement on each bus of a system is difficult to achieve either due to cost factor or due to non-existence limitation of communication facilities in some parts of the system. Moreover, as a consequence of Ohm's Law, when a PMU is placed at a bus, neighboring busses also become observable. This implies that a system can be made observable with a lesser number of PMUs than the number of busses. It is necessary to determine the minimum number of PMUs for entire observability of the power network. The optimal placement of PMUs (OPP) problem can be solved by various techniques. Hence in this paper a comprehensive review is presented on the OPP problems under the characterization of heuristic and mathematical methodologies. Also a comparison between the various methods is also presented based on the various operation and cost related factors.

关键词： Phasor measurement unit Conventional mathematical techniques artificial intelligence techniques

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