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International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 10
International Conference on Advances & Challenges in Interdisciplinary Engineering and
Management 2017 [ICACIEM 2017]
ISBN 978-81-933235-1-9 VOL 01
Website icaciem.org eMail icaciem@asdf.res.in
Received 10 – January – 2017 Accepted 28 - January – 2017
Article ID ICACIEM019 eAID ICACIEM.2017.019
Investigation on Hybrid Concrete Using Steel and
Polypropylene Fibre
P Parthiban1
1
Assistant Professor, Dept. of Civil Engineering, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India
Abstract: This project provides a summary of “Investigation on Hybrid Concrete Using Steel and Polypropylene Fibre”. The effect of hybrid fibres on
the mechanical properties of concrete mixture is studied in the present investigation. Steel fibres of 1% and polypropylene fibres 0.035% were added
together to form a hybrid fibre reinforced concrete. Mechanical properties such as compressive strength, split tensile strength and flexural strength were
determined. Hybridization refers to combination of different types of fibres. The purpose of combining the fibres is to improve the multiple properties of
concrete mixture. The behavioural efficiency of this composite material is far superior to that of plain and mono fibre reinforced concrete. The addition
of fibre is helpful to improve the fracture properties of concrete. The hybrid fibres are comprehensively being used in rigid pavements, airfield pavements,
flexible pavements, earthquake-resistant etc. The Hybrid fibre reinforced concrete cubes, cylinders and beams will be casted and finally test and find out
the compressive strength, split tensile strength and flexural strength of concrete specimens.
ISBN 978-81-933235-1-9 VOL 01
Website icaciem.org eMail icaciem@asdf.res.in
Received 10 – January – 2017 Accepted 28 - January – 2017
Article ID ICACIEM020 eAID ICACIEM.2017.020
Classification of Audio Data using Rule Classifier from
Decision Tree Induction using Training Samples
Aishwarya P1
, Gomathi M2
, Kiruthika S3
, Lalithambigai B4
, Jayaprabha P5
1,2,3,4
III MCA, 5
Professor/HOD, Department of MCA, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India
Abstract: Automatic classification of Audio Data plays major role in the fields of data mining and pattern recognition. This paper describes a rule
based classification method for multi-class audio data that is Rule based Extraction for Audio Data (READ). Typically, this technique requires
considerable computation cost to find rules from large datasets because of the combinatorial search problem. To achieve efficient and fast extraction of
classification rules, take advantage of a frequent item set mining algorithm that can exhaustively extract non-redundant and condensed patterns from a
transaction database within a reasonable time. The notable feature of this method is that the search space of classification rules can be dramatically
reduced by searching for only frequent item sets constrained by “class label item”. This paper improves the classification accuracy of a real audio dataset.
This paper is prepared exclusively for International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 2017 [ICACIEM
2017] which is published by ASDF International, Registered in London, United Kingdom under the directions of the Editor-in-Chief Dr K Samidurai and Editors
Dr. Daniel James, Dr. Kokula Krishna Hari Kunasekaran and Dr. Saikishore Elangovan. Permission to make digital or hard copies of part or all of this work for
personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this
notice and the full citation on the first page. Copyrights for third-party components of this work must be honoured. For all other uses, contact the
owner/author(s). Copyright Holder can be reached at copy@asdf.international for distribution.
2017 © Reserved by Association of Scientists, Developers and Faculties [www.ASDF.international]

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Investigation on Hybrid Concrete Using Steel and Polypropylene Fibre

  • 1. International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 10 International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 2017 [ICACIEM 2017] ISBN 978-81-933235-1-9 VOL 01 Website icaciem.org eMail icaciem@asdf.res.in Received 10 – January – 2017 Accepted 28 - January – 2017 Article ID ICACIEM019 eAID ICACIEM.2017.019 Investigation on Hybrid Concrete Using Steel and Polypropylene Fibre P Parthiban1 1 Assistant Professor, Dept. of Civil Engineering, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India Abstract: This project provides a summary of “Investigation on Hybrid Concrete Using Steel and Polypropylene Fibre”. The effect of hybrid fibres on the mechanical properties of concrete mixture is studied in the present investigation. Steel fibres of 1% and polypropylene fibres 0.035% were added together to form a hybrid fibre reinforced concrete. Mechanical properties such as compressive strength, split tensile strength and flexural strength were determined. Hybridization refers to combination of different types of fibres. The purpose of combining the fibres is to improve the multiple properties of concrete mixture. The behavioural efficiency of this composite material is far superior to that of plain and mono fibre reinforced concrete. The addition of fibre is helpful to improve the fracture properties of concrete. The hybrid fibres are comprehensively being used in rigid pavements, airfield pavements, flexible pavements, earthquake-resistant etc. The Hybrid fibre reinforced concrete cubes, cylinders and beams will be casted and finally test and find out the compressive strength, split tensile strength and flexural strength of concrete specimens. ISBN 978-81-933235-1-9 VOL 01 Website icaciem.org eMail icaciem@asdf.res.in Received 10 – January – 2017 Accepted 28 - January – 2017 Article ID ICACIEM020 eAID ICACIEM.2017.020 Classification of Audio Data using Rule Classifier from Decision Tree Induction using Training Samples Aishwarya P1 , Gomathi M2 , Kiruthika S3 , Lalithambigai B4 , Jayaprabha P5 1,2,3,4 III MCA, 5 Professor/HOD, Department of MCA, Vidyaa Vikas College of Engineering and Technology, Tiruchengode, India Abstract: Automatic classification of Audio Data plays major role in the fields of data mining and pattern recognition. This paper describes a rule based classification method for multi-class audio data that is Rule based Extraction for Audio Data (READ). Typically, this technique requires considerable computation cost to find rules from large datasets because of the combinatorial search problem. To achieve efficient and fast extraction of classification rules, take advantage of a frequent item set mining algorithm that can exhaustively extract non-redundant and condensed patterns from a transaction database within a reasonable time. The notable feature of this method is that the search space of classification rules can be dramatically reduced by searching for only frequent item sets constrained by “class label item”. This paper improves the classification accuracy of a real audio dataset. This paper is prepared exclusively for International Conference on Advances & Challenges in Interdisciplinary Engineering and Management 2017 [ICACIEM 2017] which is published by ASDF International, Registered in London, United Kingdom under the directions of the Editor-in-Chief Dr K Samidurai and Editors Dr. Daniel James, Dr. Kokula Krishna Hari Kunasekaran and Dr. Saikishore Elangovan. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honoured. For all other uses, contact the owner/author(s). Copyright Holder can be reached at copy@asdf.international for distribution. 2017 © Reserved by Association of Scientists, Developers and Faculties [www.ASDF.international]