تخمين نسبت باربري كاليفرنيا در خاك هاي مردابي به سازي شده با استفاده از شبكه عصبي مصنوعي

Estimation of California Bearing Ratio of Improved Peat Soils by Artificial Neural Networks

اﻣﺮوزه، اﺳﺘﻔﺎده از روش اﺧﺘﻼط ﻋﻤﯿﻖ ﺑﺮاي ﺑﻬﺒﻮد روﺳﺎزي ﺟﺎدهﻫﺎ ﮔﺴﺘﺮش ﯾﺎﻓﺘﻪ اﺳﺖ. ﯾﮑﯽ از ﻣﻬﻤﺘﺮﯾﻦ اﻫﺪاف اﯾﻦ روش، اﻓﺰاﯾﺶ ﺿﺮﯾﺐ ﺑﺎرﺑﺮي ﮐﺎﻟﯿﻔﺮﻧﯿﺎ و ﮐﺎﻫﺶ ﻧﺸﺴﺖ روﺳﺎزي ﻣﯽﺑﺎﺷﺪ. در ﺳﺎلﻫﺎي اﺧﯿﺮ، ﻣﺪلﺳﺎزي ﺑﻪ وﺳﯿﻠﻪ ﻫﻮش ﻣﺤﺎﺳﺒﺎﺗﯽ،ﺟﺎﯾﮕﺎه وﯾﮋهاي در ﻣﻬﻨﺪﺳﯽ ﻋﻤﺮان ﭘﯿﺪا ﮐﺮده اﺳﺖ و ﺗﺨﻤﯿﻦ رﻓﺘﺎر و ﻓﺮاﯾﻨﺪ ﻣﻘﺎومﺳﺎزي، ﮐﻪ ﺑﺎ ﭘﯿﭽﯿﺪﮔﯽﻫﺎي ﻓﺮاواﻧﯽ روﺑﺮو ﺑﻮده،ﺗﺎ ﺣﺪودي ﺑﻪ ﮐﻤﮏ اﯾﻦ روشﻫﺎ ﻣﯿﺴﺮ ﺷﺪه اﺳﺖ. ﻫﺪف اﺻﻠﯽ اﯾﻦ ﺗﺤﻘﯿﻖ، ﺳﺎﺧﺖ ﯾﮏ ﻣﺪل ﻣﺤﺎﺳﺒﺎﺗﯽ ﺟﻬﺖ ﺗﺨﻤﯿﻦ ﺿﺮﯾﺐ ﺑﺎرﺑﺮي ﮐﺎﻟﯿﻔﺮﻧﯿﺎ در ﺧﺎكﻫﺎي ﻣﺮداﺑﯽ ﻣﯽﺑﺎﺷﺪ. ﺑﺪﯾﻦ ﻣﻨﻈﻮر، اﺑﺘﺪا اﯾﻦ ﻧﻮع ﺧﺎك ﺑﺎ درﺻﺪﻫﺎي ﻣﺨﺘﻠﻔﯽ از ﺳﯿﻤﺎن و ﻣﺎﺳﻪ ﺧﻮب داﻧﻪﺑﻨﺪي ﺷﺪه و ﺑﺮ ﻣﺒﻨﺎي اﺳﺘﺎﻧﺪاردﻫﺎي ﻣﻌﺘﺒﺮ، ﻣﻘﺎومﺳﺎزي ﺷﺪه و آزﻣﺎﯾﺶﻫﺎﯾﯽ ﻧﻈﯿﺮ ﻣﻘﺎوﻣﺖ ﻓﺸﺎري ﺗﮏﻣﺤﻮره و ﺑﺎرﺑﺮي ﮐﺎﻟﯿﻔﺮﻧﯿﺎ روي ﻧﻤﻮﻧﻪﻫﺎي ﻣﻘﺎومﺳﺎزي ﺷﺪه اﻧﺠﺎم ﺷﺪ. ﭘﺲ از اﻧﺠﺎم ﺗﺴﺖﻫﺎي آزﻣﺎﯾﺸﮕﺎﻫﯽ، ﯾﮏ ﻣﺠﻤﻮﻋﻪ از اﻃﻼﻋﺎت ﺑﺮاي ﺳﺎﺧﺖ ﻣﺪل ﻫﻮش ﻣﺤﺎﺳﺒﺎﺗﯽ ﺟﻤﻊآوري ﺷﺪ. در اﯾﻦ ﺗﺤﻘﯿﻖ، از ﻣﺪل ﭘﺮﺳﭙﺘﺮون ﭼﻨﺪﻻﯾﻪ ﺑﺎ ﻣﻌﻤﺎريﻫﺎي ﻣﺨﺘﻠﻒ ﺷﺎﻣﻞ ﯾﮏ و دو ﻻﯾﻪ ﻣﺨﻔﯽ ﺑﺎ ﺗﻌﺪاد ﻧﺮونﻫﺎي ﻣﺘﻔﺎوت ﺑﺮاي ﺗﺨﻤﯿﻦ اﺳﺘﻔﺎده ﺷﺪ. ﺑﺮاي اﯾﻦ ﻫﺪف، ﭘﺎراﻣﺘﺮﻫﺎي ورودي ﺑﻪ ﻣﺪل ﺷﺎﻣﻞ ﻣﻘﺎوﻣﺖ ﻓﺸﺎري ﺗﮏﻣﺤﻮره، زﻣﺎن ﻋﻤﻞآوري و ﻣﯿﺰان ﻣﺎﺳﻪ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪﻧﺪ و آﻧﺎﻟﯿﺰ ﺣﺴﺎﺳﯿﺖ ﺑﺎ اﻟﮕﻮرﯾﺘﻢ ﮔﺎرﺳﻮن اﻧﺠﺎم ﺷﺪ. ﻧﺘﺎﯾﺞ آزﻣﺎﯾﺸﮕﺎﻫﯽ ﻧﺸﺎن داد ﮐﻪ اﻓﺰاﯾﺶ ﻣﯿﺰان ﻣﺎﺳﻪ ﺑﻪ ﻋﻨﻮان ﯾﮏ ﭘُﺮﮐﻨﻨﺪه ﻃﺒﯿﻌﯽ، ﺗﺄﺛﯿﺮ ﻗﺎﺑﻞ ﻣﻼﺣﻈﻪاي در اﻓﺰاﯾﺶ ﺑﺎرﺑﺮي ﮐﺎﻟﯿﻔﺮﻧﯿﺎ دارد. ﺑﻪ ﻋﻨﻮان ﻣﺜﺎل، در ﻣﯿﺰان ﺳﯿﻤﺎن ﺛﺎﺑﺖ 400 ﮐﯿﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮ ﻣﮑﻌﺐ، اﻓﺰاﯾﺶ ﻣﯿﺰان ﻣﺎﺳﻪ ﺑﻪ اﻧﺪازه 200 ﮐﯿﻠﻮﮔﺮم ﺑﺮ ﻣﺘﺮ ﻣﮑﻌﺐ، ﺿﺮﯾﺐ ﺑﺎرﺑﺮي ﮐﺎﻟﯿﻔﺮﻧﯿﺎ را از 31% ﺑﻪ 59% اﻓﺰاﯾﺶ داد. ﻫﻤﭽﻨﯿﻦ، ﻧﺘﺎﯾﺞ ﻣﺪلﺳﺎزي ﺑﻪدﺳﺖ آﻣﺪه ﻧﺸﺎن داد ﮐﻪ ﺑﻬﺘﺮﯾﻦ ﻣﺪل ﺑﺎ ﻣﺘﻮﺳﻂ ﻣﺮﺑﻌﺎت ﺧﻄﺎي 0/41 و ﻣﺘﻮﺳﻂ ﺿﺮﯾﺐ رﮔﺮﺳﯿﻮن 0/99 ﺑﻬﺘﺮﯾﻦ ﻋﻤﻠﮑﺮد را در ﺗﺨﻤﯿﻦ ﺿﺮﯾﺐ ﺑﺎرﺑﺮي ﮐﺎﻟﯿﻔﺮﻧﯿﺎ داﺷﺖ.

Nowadays, the use of deep soil mixing method has expanded to improve road pavement. One of the most important goals of this approach is to increase the California Bearing Factor and reduce the pavement settlement. In recent years, modeling by computational intelligence has found a special place in civil engineering, and partly due to the use of these methods, the behavior and process of stabilization, which has been encountered with many problems, is possible. The main objective of this research is to develop a computational model for estimating the California Bearing Factor for peat soils. For this purpose, firstly, this soil was mixed with various percentages of cement and well-graded sand based on validated standards, and experimental tests such as single-axial compressive strength and California bearing were performed on the stabilized specimens. After laboratory testing, a set of information was compiled to construct the computing intelligence model. In this research, the multi-layer (MLP) with different architectures including one and two hidden layers with different number of neurons were used for estimation. For this purpose, input parameters of uniaxial compressive strength, curing time and the amount of sand were considered and the sensitivity analysis was carried out using the Garson algorithm. The experimental results of this study showed that by increasing the amount of sand as a natural filler a significant effect on California's bearing ratio was observed. For example, in a constant amount of cement of 400 kg/m3, adding 200 kg/m3 sand, increased the CBR from 31% to 59%. Also, the results showed that the best model with an average mean square error of 0.41 and an average regression coefficient of 0.99 showed the best performance in estimation of the California Bearing Factor.