Predicting house damage class using artificial intelligence method
Osman-Schlegel, N. Y. 2013, Predicting house damage class using artificial intelligence method, in HKICEAS 2013 : Proceedings of the 2013 Engineering and Applied Science Hong Kong International Conference, [Conference], [Hong Kong, China], pp. 486-491.
The unsatisfactory performance of light structures founded on expansive soils subject to seasonal movements is frequently reported since the early 1950's in Australia. Excessive movements have caused damage to numerous structures that have not been adequately designed to accommodate soil volume changes. However, the sole presence of expansive soil is not necessarily the main cause of damage. Other factors such as vegetation, climate factors, types of construction materials and geology type may also contribute. This paper presents a model which predicts the damage class by analyzing combinations of the contributing factors using artificial intelligence methods. This model can help to identify if any serious and urgent repairs are necessary and immediate actions could be initiated without delay.
Field of Research
129999 Built Environment and Design not elsewhere classified
Socio Economic Objective
970112 Expanding Knowledge in Built Environment and Design
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