Optimizing compressed Earth blocks mix design incorporating rice straw and cement using artificial neural network

College

Gokongwei College of Engineering

Department/Unit

Civil Engineering

Document Type

Conference Proceeding

Source Title

HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management

Volume

2018-January

First Page

1

Last Page

6

Publication Date

7-2-2017

Abstract

Compressed earth blocks (CEB) in construction is an alternative way of promoting sustainable construction building materials. Compressed earth blocks are used as replacement to concrete masonry wall units. It has many advantages in terms of material cost, thermal properties, embodied energy, sound and fire proofing. In this study, production of 250 CEBs was made with dimension of 290mm×140mm ×100mm. With the available data, Self-Organizing Map (SOM) toolbox was used to classify results according to the parameters that have similarities. The groupings that were classified through SOM were observed, analysed and was related to the compressive strengths of CEBs. Two Self Organizing Map (SOM) Models were derived in the study. These are Model E and Model F. Model E and Model F contains 2 input parameter. Model E has 4 classifications: Group A and C classified CEBs that are above the strength requirement of Philippine National Standard (PNS). Group B and D clustered the CEBs with the lowest compressive strength value. Overall, Model E clustered the groups into similar characteristics. Model E showed that CEB with 10% cement and above with any fiber content conforms to the requirement of PNS under TYPE 2 CHB. Model F has also 4 classifications: Group A and C classified CEBs that are above the strength requirement of PNS. Group B and D clustered the CEBs with the lowest compressive strength value. Model F showed that any fiber content can be used in combination with 10% or more Cement to achieve the requirement of PNS Type 2 CHB. © 2017 IEEE.

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Digitial Object Identifier (DOI)

10.1109/HNICEM.2017.8269450

Disciplines

Civil Engineering

Keywords

Earth construction; Self-organizing maps; Neural networks (Computer science)

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