Data Mining Practical Machine Learning Tools and Techniques

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Thank you for downloading linear and nonlinear programming. As you may know, people have search numerous times for their favorite novels like this linear and nonlinear programming, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their desktop computer. linear and nonlinear programming is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the linear and nonlinear programming is universally compatible with any devices to read.

Linear And Nonlinear Programming

Mechanical properties of concrete at high temperature—A review

Several types of industrial byproducts are generated. With increased environmental awareness and its potential hazardous effects, utilization of industrial byproducts has become an attractive alternative to disposal. One such by-product is silica fume (SF), which is a byproduct of the smelting process in the silicon and ferrosilicon industry. Silica fume is very effective in the design and development of high strength high performance concrete. This paper covers the physical, chemical properties of silica fume, and its reaction mechanism. It deals with the effect of silica fume on the workability, porosity, compressive strength, splitting tensile strength, flexural strength, creep and shrinkage of concrete.

Utilization of silica fume in concrete: Review of hardened properties

This paper presents the results of an experimental investigation on the effect of different amounts of silica fume (SF) and water to cement ratios (w/c) on the residual compressive strength of high-strength concrete after exposure to high temperatures. Based on the results obtained the rates of strength loss for concrete specimens containing 6% and 10% SF at 600 °C were 6.7% and 14.1% lower than those of the ordinary concrete. The dosage of SF had no significant effect on the relative residual compressive strength at 100 and 200 °C, whereas the amount of SF had considerable influences on the residual compressive strength above 300 °C similar to the response at 600 °C. The optimum dosage of SF and w/c was found to be 6% and 0.35, respectively.

Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures

Prediction of pavement condition is one of the most important issues in pavement management systems. In this paper, capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analysed in three levels: in 1 year, in 2 years (short term) and in the pavement life cycle (long term). For this purpose, three effective groups on pavement condition including traffic conditions, environmental changes and pavement structures were studied and nine effective variables were selected as input variables. International roughness index (IRI) was also used as the indicator of pavement performance. Various ANN structures and GMDH models in partial description configurations of 1st, 2nd, 3rd and 4th polynomials were formed and examined. Results indicate that while ANN models predict future condition of pavement with high accuracy in the short and long terms, GMDH models do not have accepted accuracy.

Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks

The objective of this study was to evaluate the fatigue and rutting performance of hot mix asphalt (HMA) mixtures prepared by bentonite-modified bitumen. As a modifier of bitumen, 10%, 15%, 20%, 25% and 30% bentonite (BT) content by weight of bitumen was used. Unmodified and modified asphalt binders were subjected to physical and conventional tests (penetration, softening point and ductility). In this research, performance tests, such as Marshall stability, resilient modulus, dynamic creep and wheel track tests, were conducted on the modified and unmodified mixtures. Also, the fatigue life of asphalt mixtures was evaluated through four-point bending test in strain-controlled mode at three microstrain levels (600–800–1000 lm/m) with sinusoidal loading. The fatigue life of mixtures has been evaluated based on the 50% reduction in the initial stiffness modulus. The results show that addition of BT improves the physical properties of bitumen and also can increase the rutting resistance and the shear strength of HMA. In addition, the fatigue life of asphalt mixtures prepared with bentonite-modified bitumen is longer than that of the conventional HMAs.

Hasan Ziari

Papers

Effects of silica fume addition and water to cement ratio on the properties of high-strength concrete after exposure to high temperatures

Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks

Investigation of fatigue and rutting performance of hot mix asphalt mixtures prepared by bentonite-modified bitumen

Evaluation of fatigue behavior of hot mix asphalt mixtures prepared by bentonite modified bitumen