Geoteknik mühendisliğinde yapay sinir ağı uygulamaları ve bir örnek: Zemin profilinin tahmin edilmesi.

The use of artificial neural networks (ANN) in geotechnical engineering has gained wide application in Turkey as well as the world during the past ten years. A comprehensive literature survey has shown that applications are concentrated in basic areas such as classification, site characterization, liquefaction, hydraulic conductivity, compaction, consolidation as well as practice comprising the problems of retaining structures, settlement of foundations, pile capacity and modeling of soil behavior where the relation-ship among the several parameters involved is not always thoroughly understood. Several case histories are presented as examples. Latest research shows that artificial neural networks are heading towards unison with fuzzy logic and genetic algorithms and it is certainly superior to the statistical methods . The second part of the paper gives an account of research conducted into estimation of the soil profiles in the city of Adapazari, Turkey. There has been widespread damage and destruction in the city during the Mw=7.4 earthquake in 1999. The damage has largely been blamed on inferior alluvial deposits and parts of the city have been moved to the North where the soil was found to be "sound". The sediments in the city are the products of the meandering river Sakarya which also flooded the region almost biannually. The two processes have resulted in the formation of complex soil profiles and near chaotic profiles are frequent. The silty layers are possibly the source of ground failures, occasionally leading to liquefaction in the absence of sands. A comprehensive soil investigation has been carried out since 1990 by boreholes and cone penetration soundings. The authors have used the rich database available established from previous and current laboratory and field investigations. Out of this voluminous data those pertaining to depths of 2 to 7m have been used for the ANN work, as those depths have been diagnosed as the possible liquefaction zone. Data from 117 CPT sites whose coordinates were known were employed for this study. The 3236 readings of tip resistance and sleeve friction were used to establish the ANN model. The well established Robertson classification chart defines nine types of soil. It requires the normalised values of tip resistance(Qt) and and sleeve friction(Fr) a identify the soil layer. The Qt* and Fr* are further defined to form the spatial distribution by the use of equations. The training matrix even for a limited depth of 5m using the data from 90 CPTU tests came out to be of size 3236x3236, which was difficult to handle. Consequently, thirteen 1800 by 1800 matrices were established (1.60-1.98m, 2.00-2.38m, 2.40-2.78m, 2.80-3.18m, 3.20-3.58m, 3.60-3.98m, 4.00-4.38m, 4.40-4.78m, 4.80-5.18m, 5.20-5.58m, 6.00-6.38m, 6.40-6.78m, 6.80-6.98m). Data from 27 CPTU were used to form the thirteen 540 by 1800 simulation matrices and 1800 by 1800 training matrices. Analyses were carried out on the Matlab 2010a Toolbox7 NNtraintool interface. 60% of the data were employed for Training, 15% for Validation and 15% for Testing. Inspecting the results, it was found that the success rate in estimating the soil profile anywhere in the 26 km² city area was as high as 92%. This is a surprisingly high success rate considering the highly complex and laterally variable soil profiles throughout the city. [ABSTRACT FROM AUTHOR]