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A platform for research: civil engineering, architecture and urbanism
Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)
Abstract Nowadays, grouting plays a vital role in dam foundation. The purpose of the grouting with pressure is to fill the joints, discontinuities, void distance and cavities in the rock masses to consolidate and caulk the rock masses for reducing seepage and uplift pressure in the dam foundation and related structures. Cheraghvays dam is located at 17 km away from the west of the Saqqez in Kurdistan province of Iran. In the Cheraghvays dam, the grout holes were arranged at 2 m intervals and with 10.5 m final depth. There are three grout sections (0–2.5, 2.5–5.5, 5.5–10.5 m) inside each grout hole and the grout process was conducted from the bottom to the top. Finally, the controlling holes are used to perform Lugeon test and to check the grouting quality of the dam foundation. Checking the operations in all areas of the foundation causes cost and time consuming. In this paper, experimental variogram and their mathematical model are calculated by geostatistics methods. To assess consolidation grouting quality, secondary permeability was predicted by artificial neural network (ANN) and the linear regression method. For this aim, datasets of 68 blocks which include first permeability, cement take and secondary permeability have been collected. The obtained results have indicated that ANN can predict secondary permeability in Cheraghvays dam foundation better than the linear regression method. So, ANN can be used in consolidation grouting quality assessment.
Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)
Abstract Nowadays, grouting plays a vital role in dam foundation. The purpose of the grouting with pressure is to fill the joints, discontinuities, void distance and cavities in the rock masses to consolidate and caulk the rock masses for reducing seepage and uplift pressure in the dam foundation and related structures. Cheraghvays dam is located at 17 km away from the west of the Saqqez in Kurdistan province of Iran. In the Cheraghvays dam, the grout holes were arranged at 2 m intervals and with 10.5 m final depth. There are three grout sections (0–2.5, 2.5–5.5, 5.5–10.5 m) inside each grout hole and the grout process was conducted from the bottom to the top. Finally, the controlling holes are used to perform Lugeon test and to check the grouting quality of the dam foundation. Checking the operations in all areas of the foundation causes cost and time consuming. In this paper, experimental variogram and their mathematical model are calculated by geostatistics methods. To assess consolidation grouting quality, secondary permeability was predicted by artificial neural network (ANN) and the linear regression method. For this aim, datasets of 68 blocks which include first permeability, cement take and secondary permeability have been collected. The obtained results have indicated that ANN can predict secondary permeability in Cheraghvays dam foundation better than the linear regression method. So, ANN can be used in consolidation grouting quality assessment.
Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)
Zadhesh, Jamal (author) / Rastegar, Fuad (author) / Sharifi, Faraydoun (author) / Amini, Hasel (author) / Nasirabad, Hossein Mirzaei (author)
Indian Geotechnical Journal ; 45 ; 136-144
2014-04-12
9 pages
Article (Journal)
Electronic Resource
English
Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)
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