• Structural Engineering and Mechanics
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• v.71 no.3
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• pp.317-327
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• 2019

The aim of this paper is to present the most important issues on the implementation, operation and maintenance of foundation for machines. The article presents the newest solutions both in terms of technology implementation as well as materials used in construction of such structures. Foundations for machines are special building structures used to transfer loads from an operating machine to the subsoil. The purpose of these foundations is not just to transfer loads, but also to reduce vibrations occurring during operation of the machine, i.e. their damping and preventing redistribution to other elements of the building. It should be noted that foundations for machines (particularly foundations for hammers) are the most dynamically loaded building structures. For these reasons, they require precise static and dynamic calculations, accuracy in their implementation and care for them after they have been made. Therefore, the paper in detail present the guidelines regarding: design, construction and maintenance of structures of this type. Furthermore, the most important parameters and characteristics of materials used for the construction of these foundations are described. As a result of the conducted analyzes, it was found that the concrete mix, in foundations for machines, should have a low water/binder ratio. For its execution, it is necessary to use broken aggregates from igneous rocks and binders modified with mineral additives and chemical admixtures. On the other hand, the reinforcement of composites should contain a large amount of structural reinforcement to prevent shrinkage cracks.

• Geomechanics and Engineering
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• v.20 no.4
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• pp.345-358
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• 2020

Numerous studies have repeatedly demonstrated the efficiency of using skirts to increase the bearing capacity and to reduce settlement of shallow foundations subjected to static loads. However, no efforts have been made to study the efficiency of using these skirts to reduce settlement produced by machine vibration, although machines are very sensitive to settlement and the foundations of these machines should be designed properly to ensure that the settlement produced due to machine vibration is very small. This research has been conducted to investigate the efficiency of using skirts as a technique to reduce the settlement of a strip foundation subjected to machine vibration. A two-dimensional finite element model has been developed, validated, and employed to achieve the aim of the study. The results of the analyses showed that the use of skirts reduces the settlement produced due to machine vibration. However, the percentage decrease of the settlement is remarkably influenced by the density of the soil and the frequency of vibration, where it rises as the frequency of vibration increases and declines as the soil density rises. It was also found that increasing skirt length increases the percentage decrease of the settlement. Importantly, the results obtained from the analyses have been utilized to derive new dynamic impedance values that implicitly consider the presence of skirts. Finally, novel design equations of dynamic impedance that implicitly account to the effect of the skirts have been derived and validated utilizing a new intelligent data driven method. These new equations can be used in future designs of skirted strip foundations subjected to machine vibration.

• Geomechanics and Engineering
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• v.1 no.2
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• pp.121-142
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• 2009

The paper deals with the applications of spectral finite element method to the dynamic analysis of framed foundations supporting high speed machines. Comparative performance of approximate dynamic stiffness methods formulated using static stiffness and lumped or consistent or average mass matrices with the exact spectral finite element for a three dimensional Euler-Bernoulli beam element is presented. The convergence of response computed using mode superposition method with the appropriate dynamic stiffness method as the number of modes increase is illustrated. Frequency proportional discretisation level required for mode superposition and approximate dynamic stiffness methods is outlined. It is reiterated that the results of exact dynamic stiffness method are invariant with reference to the discretisation level. The Eigen-frequencies of the system are evaluated using William-Wittrick algorithm and Sturm number generation in the $LDL^T$ decomposition of the real part of the dynamic stiffness matrix, as they cannot be explicitly evaluated. Major's method for dynamic analysis of machine supporting structures is modified and the plane frames are replaced with springs of exact dynamic stiffness and dynamically flexible longitudinal frames. Results of the analysis are compared with exact values. The possible simplifications that could be introduced for a typical machine induced excitation on a framed structure are illustrated and the developed program is modified to account for dynamic constraint equations with a master slave degree of freedom (DOF) option.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1367-1374
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• 2022

In this paper, we propose an automatic cosmetic foundation recommendation system that suggests a good foundation product based on the user's skin color. The proposed system receives and preprocesses user images and detects skin color with OpenCV and machine learning algorithms. The system then compares the performance of the training model using XGBoost, Gradient Boost, Random Forest, and Adaptive Boost (AdaBoost), based on 550 datasets collected as essential bestsellers in the United States. Based on the comparison results, this paper implements a recommendation system using the highest performing machine learning model. As a result of the experiment, our system can effectively recommend a suitable skin color foundation. Thus, our system model is 98% accurate. Furthermore, our system can reduce the selection trials of foundations against the user's skin color. It can also save time in selecting foundations.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.455-462
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• 1998

Analysis and design of vibrating machine foundations subjected to dynamic loads is a very complex problem. Thus it is difficult to set up an accurate analytical modeling. Generally, the design of foundations for vibrating machines has been performed by the equivalent static analysis which is generally based on engineer's experience and various assumptions The purpose of this study is to develop an integrated system which enables structural engineers to produce results of high quality within a short time in works related to structural analysis and design of foundation for vibrating machines. As the result of this study, level-up of application software is expected as well as improvement of quality in structural engineering and reduction of engineers' effort.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.306-312
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• 2006

Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.130-133
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• 1996

An exploration for an emotional intelligence paradigm has been delineated. Emotional intelligence is investigated in terms of composing machine as a modern abstract art. The system consists of interface, plan and performance modules. Design concepts of the system are modular, open, and user friendly to ensure the overall performance. The exploration of art in the view of intelligence, information and structure will restore the balanced sense of the art and the science seek the happiness of life. The investigations of emotional intelligence will establish the foundations of intelligence, information and control technologies.

• Earthquakes and Structures
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• v.12 no.6
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• pp.629-641
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• 2017

Foundation plays a significant role in safe and efficient turbo machinery operation. Turbo machineries generate harmonic load on the foundation due to their high speed rotating motion which causes vibration in the machinery, foundation and soil beneath the foundation. The problems caused by vibration get multiplied if the soil is poor. An improperly designed machine foundation increases the vibration and reduces machinery health leading to frequent maintenance. Hence it is very important to study the soil structure interaction and effect of machine vibration on the foundation during turbo machinery operation in the design stage itself. The present work studies the effect of harmonic load due to machine operation along with earthquake loading on the frame foundation for poor soil conditions. Various alternative foundations like rafts, barrette, batter pile and combinations of barrettes with batter pile are analyzed to study the improvements in the vibration patterns. Detailed computational analysis was carried out in SAP 2000 software; the numerical model was analyzed and compared with the shaking table experiment results. The numerical results are found to be closely matching with the experimental data which confirms the accuracy of the numerical model predictions. Both shake table and SAP 2000 results reveal that combination of barrette and batter piles with raft are best suitable for poor soil conditions because it reduces the displacement at top deck, bending moment and horizontal displacement of pile and thereby making the foundation more stable under seismic loading.

• Geomechanics and Engineering
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• v.18 no.5
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• pp.493-502
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• 2019

Internal stability is an important safety issue for levees, embankments, and other earthen structures. Since a large part of the world's population lives near oceans, lakes and rivers, floods resulting from breaching of dams can lead to devastating disasters with tremendous loss of life and property, especially in densely populated areas. There are some main factors that affect the internal stability of dams, levees and other earthen structures, such as the erodibility of the soil, the water velocity inside the soil mass and the geometry of the earthen structure, etc. Thus, the mechanism of internal erosion and stability of soils is very complicated and it is vital to investigate the assessment methods of internal stability of soils in embankment dams and their foundations. This paper presents an improved support vector machine (SVM) model to predict the internal stability of soils. The grid search algorithm (GSA) is employed to find the optimal parameters of SVM firstly, and then the cross - validation (CV) method is employed to estimate the classification accuracy of the GSA-SVM model. Two examples of internal stability of soils are presented to validate the predictive capability of the proposed GSA-SVM model. In addition to verify the effectiveness of the proposed GSA-SVM model, the predictions from the proposed GSA-SVM model were compared with those from the traditional back propagation neural network (BPNN) model. The results showed that the proposed GSA-SVM model is a feasible and efficient tool for assessing the internal stability of soils with high accuracy.