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\title{Predicting Dry Density of Soil from Some Physical and Chemical Properties}
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             \author[1]{Dhurgham Abdul-Jaleel Rasool  Al-Hamdani}

             \author[2]{Hana Mahmoud Amer  Al-Kasaar}

             \author[3]{Hussain Ali Muhammad  Zani}

             \affil[1]{  Al-Furat Al-Awsat Technical University}

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\date{\small \em Received: 14 December 2017 Accepted: 1 January 2018 Published: 15 January 2018}

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\begin{abstract}
        


The prediction of dry density play important role in projects or major or laboratory tests which required to estimates the value of dry density for cohesion less soils to decision of the solution of these soils or conducting tests. In this paper, an attempt utilized to predict the value cohesion less soil form known some physical and chemical properties of soil such as (LL,PL,PI,?,Gs,F200,TSS,SO3 and OM). The data utilized in this study is toked from Al-Najaf technical institute Laboratory which conducted for ninety nine cohesion less soil samples. These tests may classify aseasier, cheaper and low time consume when compared with laboratory dry density tests. The results show many correlation models depending on the independent variables constricted to estimate the dry density of soil. The highest coefficient of determination resulted from this study is 0.92 for multiple regression analysis. In this case nine soil property correlated to gathers to estimate the dry density. This value may decrease when the independent variable are decrease than nine soil property.

\end{abstract}


\keywords{regression, liquid limit, plastic limit, correlation, water content, density.}

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\let\tabcellsep& 	 	 		 
\section[{I. Introduction}]{I. Introduction}\par
oil density, characterized as the evident density of field soil and ascertained from the stove dry mass per unit volume of field soil, is an imperative soil property that outlines general soil auxiliary attributes. It is a crucial information necessity for all intents and purposes every numerical model portraying the exchange and connection of soil substance constituents inside the ecosphere. Mass thickness is a generally straight-forward property to gauge and a number of broad datasets have been accumulated \hyperref[b0]{(Hall et al., 1977;}\hyperref[b1]{Rawls et al., 1981)}. Along these lines, few endeavors have been made to create approachesp;o0 for its forecast from other essential soil properties. Be that as it may, the expanding enthusiasm for creating far reaching national datasets of soil physical properties for use in spatially-or stochastically-based ecological demonstrating \hyperref[b2]{(King et al., 1995;}\hyperref[b3]{Bruand et al., 1996)} has unavoidably featured discontinuities in the current estimated datasets. This thusly, has now centered consideration around the need to create algorithmic techniques that can anticipate variety in mass thickness as indicated by the consistent variety of soil properties, for example, molecule size and natural issue content. A few researchers focusing to estimate soil density depending on its physical and chemical properties empirically. Simple and Multiple linear regression were utilized for correlation the physical and chemical properties with soil density. Some of this relationship is shown in Table \hyperref[b0]{(1)}. In this table, the researchers developed limited number of empirical formulae while other researchers focusing on presenting the general behavior of the relation between density with chemical and physical properties. Most of correlation that publish pure empirical formulae which is created byutili zings Data Analysis Tool Bar in Microsoft Excel. As a sample of the relation which explain the general behavior is the relation developed by \hyperref[b4]{Tanveera A. et al. (2016)}; they correlate bulk density with many soil property like (texture, organic matter, and mineral friction as sand, silt, and clay). Twenty five soil samples collected from different a location in Kashmir valley in India. The depth of collected samples ranged between 20 to 35 cm. they conclude that the relation between bulk density and organic matter, porosity, and present of clay minerals are positive with present of sand. The relation of the physical and chemical properties with soil bulk density as mentioned by Traveera et al. are shown in models are shown in (Table \hyperref[tab_0]{1}). This relation created byusing Microsoft Excel. \hyperref[b5]{Andres A. (2004)}, he analyze eight sandy soil samples by conducting maximum dry density, soil classification and measuring the fines content and the uniformity coefficient of these samples. He correlate some of physical properties with the maximum dry density. The correlation were measured and some specific behavioral patterns were encountered and analyzed. He conclude that the correlation between well graded sands and maximum dry density have high coefficient of determination, while the poorly graded sand is lower. Thecorrelation model sproposed by Andres A. was developed using Data Analysis Tool Bar in Microsoft Excel. These correlations are shown in Table \hyperref[b0]{(1)}. Chaudhari, P. R. et al. (2013), They investigate the relations of bulk density of soil with texture, organic matter content have available quantity of macro and micro nutrient. Eight soil samples utilized in this investigation. They conclude that the relationships with all soil properties under investigation are negative relation except the relation with sand content. Besides texture and optimum moisture content, organic matter was also the most effective factor affected on the bulk density of soils. The concluded relation was developed using Data Analysis Tool Bar in Microsoft Excel. These correlations are shown in Table \hyperref[b0]{(1)}.S.H. \hyperref[b8]{Hallett, et al. (1998)}they utilized the procedure of  {\ref Rawls (1983)} to estimate the bulk density of 1568 soil samples within Wales and England. The present of sand, silt, clay, organic matter and the bulk density were the available data utilize in these procedure. The principle of Rawls procedure is predicting bulk density as dependent variable on other soil properties as independent variables. The relations developed by utilizing Rawls procedure are presented in Table \hyperref[b0]{(1)}. The main purpose of this work is to develop a new correlation system using regression analysis to predict the dry density of soil from physical and chemical properties. The outcomes of this work can be summarized as Develop many simple and multiple correlations model to predict dry density by using regression analyses to decide the best correlation may use to estimate the value of dry density. 
\section[{II. Materials and Methods}]{II. Materials and Methods}\par
The soil which used in this study is collected from site investigation reports. The soil sample includes different size collected from different locations in Al-Najaf Al-Ashraf City. A ninety-nine of disturbed soil samples were used in this study. The samples were taken from reports of pavement projects and exploration reports during the period from 2005 to 2017. The reports are prepared by scientific and advisory consultant bureauin Al-Najaf Technical Institute. All the tests in reports prepared according to ASTM standards. The selected soil samples include plastic and non-plastic materials. The soil parameters which collected and utilized in the database include organic matter (OM), total suspended solids (TSS), sulfate content (SO 3 ), natural water content (?), present fines (F\#200), liquid limits (LL), plastic limits (PL), plasticity index (PI), specific gravity (G S ), and dry density (? dry or ? d ). So as to survey the ampleness of the database, engaging measurements of each dataset exhibits in the database were resolved. Table  {\ref (}2) introduces the elucidating insights of every factor. While the histogram conveyance of the database is appeared in Figure \hyperref[b0]{(1)}. As per the outcomes that show up in Table \hyperref[b1]{(2)}, it can be inferred that the database comprises of an accessible scope of information. In this manner, this database can be utilized for the examination of the execution of existing observational formulae with the correct esteem.     
\section[{III. Results and Discussion}]{III. Results and Discussion}\par
Relapse examination is a factual procedure used to assess the connections between factors. It is utilized to comprehend which one of the reliant factors are identified with the free factor and to investigate the types of these connections. Both Single regression analysis (SRA) and multiple regression analysis (MRA) were created in this examination to appraise the value of dry density in view of a portion of the physical and chemical properties by utilizing the chose database.\par
The trucks choice from Excel was utilized to chart the qualities acquired from the analyses, it was likewise connected an element that is equipped for including a non-straight pattern line arrangement of focuses. The pattern line is a bend characterized from pre-decided capacities, for example, Polynomial, Logarithmic, Power and Exponential. Additionally, the R-squared, known as the coefficient of assurance, can be computed. The R-squared esteem is a pointer that reaches from 0 to 1 and uncovers how intently the assessed esteems from the pattern line compare to the genuine information. The pattern line is more solid when its R-squared esteem is at or close to 1. The chose slant line was unified with the most elevated R-squared esteem. The power work was the nearest guess to the arrangement of focuses got from the tests, this condition has a highest R-squared estimation. 
\section[{a) Simple Regression Analysis}]{a) Simple Regression Analysis}\par
SRA is the most commonly basic type of regression and utilized in the predictive analysis. There are two things represent the main idea of simple regression analysis: the first is providing the set of predictor variables with good accuracy in predicting an outcome value of the variable, the second, is providing significant predictors variable as a dependent variable. To establish a simple regression between dry density and physical and chemical soil properties, many point are drawn as the (X) coordinate represent the specified soil property and the (Y) coordinate represent the dry density. The best fit line pass through and discussed the variation of most point is the simple regression line, the equation of this line simulate the relation between soil property utilized and dry density. The accuracy of SRA measured by calculating the coefficient of determination (R2). It is a number which explained the reliability of proposed proportion. The coefficient of determination ranged between 0 to 1. The best correlation is the correlation has the coefficient of determination closest to 1. Practically, the value of coefficient of determination equal or greater than 0.8 indicates the acceptable correlation. To develop the models of SLRA on the available database. Data Analysis Tool Bar in Microsoft Excel is utilized. The dry density of soil specified as the dependent variable and other soil properties such as (LL, PL, PI, ?, TSS, OM, and SO 3 ) specified as independent variable individually. SLRA models for the  
\section[{E}]{E}\par
The correlation formulae and the coefficient of determination are presented in Table \hyperref[b2]{(3)}. As shown in Table \hyperref[b3]{(4)}, model 5 has given the closest coefficient of determination to 1while model 9 given the closest coefficient of determination to 0.        
\section[{IV. Conclusion}]{IV. Conclusion}\par
Depending on the results of the correlation above, the following points may be concluded: 1. Some soil properties put high coefficient of determination with dry density such as specific gravity and plasticity index while other soil properties put low coefficient of determination such as liquid limit, moisture content, total soluble salts, and plastic limits. This indicating accepted mean, that the soil with higher specific gravity must be higher in dry density. 2. The correlation using more than one soil properties give higher than when using one soil properties. 3. When using effective soil properties in multiple correlation, the coefficient of determination get higher. 4. When increase the samples which are utilized in correlation, the coefficient of determination get higher.       \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.10795\textwidth}P{0.39015\textwidth}P{0.06885\textwidth}P{0.16235\textwidth}P{0.12069999999999999\textwidth}}
Researchers\tabcellsep Level of Significance\tabcellsep ?? ??\tabcellsep Function\tabcellsep Density function of\\
\tabcellsep Significant increase\tabcellsep 0.60\tabcellsep -\tabcellsep Sand \%\\
Tanveera A.\tabcellsep Significant decrease\tabcellsep 0.41\tabcellsep -\tabcellsep Clay \%\\
et al. (2016)\tabcellsep Significant decrease\tabcellsep 0.75\tabcellsep -\tabcellsep O.M. \%\\
\tabcellsep Significant decrease\tabcellsep 0.52\tabcellsep -\tabcellsep n\\
\tabcellsep Significant increase\tabcellsep 0.906\tabcellsep ? d = 87.715(C u ) 0.166\tabcellsep Clean sand \%\\
\tabcellsep Slightly in poorly graded sand\tabcellsep \tabcellsep \tabcellsep \\
Andres A. (2004)\tabcellsep and Significantly in poorly graded sand Significantly increase and then\tabcellsep -\tabcellsep -\tabcellsep \%Fines\\
\tabcellsep slightly increase in low and high\tabcellsep -\tabcellsep -\tabcellsep \%Fines\\
\tabcellsep plasticity Clay.\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Significant increase\tabcellsep 0.909\tabcellsep -\tabcellsep Sand\%\\
Chaudhari, P. R. et al. (2013)\tabcellsep Significant decrease Significant decrease Significant decrease Significant decrease\tabcellsep 0.633 0.734 0.886 0.495\tabcellsep ----\tabcellsep Clay\% Silt\% n CaCO3\\
\tabcellsep Significant decrease\tabcellsep 0.661\tabcellsep -\tabcellsep EC\\
\tabcellsep Slightly decrease\tabcellsep 0.2317\tabcellsep -\tabcellsep pH\\
\tabcellsep Significant decrease\tabcellsep 0.887\tabcellsep -\tabcellsep OMC\\
S.H. Hallett, J.M Hollis and C.A. Keay (1998),\tabcellsep For 8 samples For 16 samples\tabcellsep 0.65 0.64\tabcellsep ? b = 0.100LogeClay + 0.0195LogeSand -0.618 + 0.095LogeSilt + 0.178Loge OM ? b = 5.01 -0.931LogeSilt + 0.038LogeClay -0.173LogeSand -0.365Loge OM\tabcellsep Silt,Clay, Sand, OM Silt, Clay, Sand, OM\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
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\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.114673157162726\textwidth}P{0.07920723226703755\textwidth}P{0.06738525730180807\textwidth}P{0.5887343532684284\textwidth}}
SRA\tabcellsep Independent variables\tabcellsep R 2\tabcellsep Developed empirical formulae\\
Model 1\tabcellsep ????\tabcellsep 0.7258\tabcellsep ???? = 0.0005???? 2 ? 0.0063???? + 1.5682\\
Model 2\tabcellsep ????\tabcellsep 0.5216\tabcellsep ???? = ?0.0001???? 3 + 0.0055???? 2 ? 0.0361???? + 1.5741\\
Model 3\tabcellsep ????\tabcellsep 0.7803\tabcellsep ???? = 0.0018???? 2 ? 0.011???? + 1.5754\\
Model 4\tabcellsep ????\tabcellsep 0.8654\tabcellsep ???? = 0.1753?? 0.8608 ????\\
Model 5\tabcellsep ð??"ð??"\tabcellsep 0.6806\tabcellsep ???? = 1.4809?? 0.0082ð??"ð??"\\
Model 6\tabcellsep ??200\tabcellsep 0.4516\tabcellsep ???? = 0.0001(??200) 2 + 0.0008(??200) + 1.6211\\
Model 7\tabcellsep ??????\tabcellsep 0.7065\tabcellsep ???? = 0.0056?????? 3 ? 0.0254?????? 2 ? 0.0891?????? + 2.0156\\
Model 8\tabcellsep ????3\tabcellsep 0.4953\tabcellsep ???? = ?0.0027(????3) 5 + 0.0369(????3) 4 ? 0.1757(????3) 3 + 0.3842(????3) 2 ?0.4438(????3) + 1.9374\\
Model 9\tabcellsep ????\tabcellsep 0.2344\tabcellsep ???? = ?0.0032(????) 3 + 0.0515(????) 2 ? 0.2004(????) + 1.9118\\
\multicolumn{2}{l}{b) Multiple Regression Analysis}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.05756207674943567\textwidth}P{0.20146726862302483\textwidth}P{0.006715575620767494\textwidth}P{0.02590293453724605\textwidth}P{0.558352144469526\textwidth}}
MRA\tabcellsep \multicolumn{2}{l}{Independent variables}\tabcellsep R 2\tabcellsep Developed empirical formulae\\
Model 10\tabcellsep \multicolumn{2}{l}{LL, PL, PI, ?, Gs, F200, TSS, SO3 and OM}\tabcellsep 0.92\tabcellsep ?d = ?0.32331 + 0.090914 LL ? 0.0947PL ? 0.08925PI + 0.000214 ? + 0.812355Gs + 0.001807F200 ? 0.06588TSS + 0.028297SO3 + 0.008072 OM\\
Model\tabcellsep LL,\tabcellsep PL, PI,\tabcellsep 0.90\tabcellsep ?d = ?1.313028295 + 0.095710046LL ? 0.09471365PL ? 0.092730055PI\\
11\tabcellsep \multicolumn{2}{l}{?, Gs, and F200}\tabcellsep \tabcellsep + 0.001277011 ? + 1.097524369Gs + 0.001872137F200\\
Model 12\tabcellsep \multicolumn{2}{l}{LL, PL, PI, ?, and Gs}\tabcellsep 0.89\tabcellsep ?d = ?1.408337645 + 0.106414816 LL ? 0.104265638 PL ? 0.104753722 PI + 0.002239171 ? + 1.146455612 Gs\\
Model 13\tabcellsep \multicolumn{2}{l}{TSS, SO3, and OM}\tabcellsep 0.80\tabcellsep ?d = 2.042655154 ? 0.109336005 TSS + 0.037814019 SO3 ? 0.00146424 OM\\
Model 14\tabcellsep LL, PL, and PI\tabcellsep \tabcellsep 0.73\tabcellsep ?d = 1.445378617+0.112257516 LL ? 0.120009326 PL ? 0.078119235 PI\\
Model 15\tabcellsep ?, Gs, and F200\tabcellsep \tabcellsep 0.84\tabcellsep ?d = ?1.886748318+0.00075222 ? + 1.328356584 Gs + 0.002640785F200\\
\tabcellsep 2.3\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 2.2\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 2.1\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 2\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 1.9\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 1.8\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 1.7\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 1.6 Real\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep 1.5\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{3}{l}{1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3}\tabcellsep \\
\multicolumn{2}{l}{Figure 11:}\tabcellsep \tabcellsep \tabcellsep Figure 12:\end{longtable} \par
 
\caption{\label{tab_4}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.053125\textwidth}P{0.796875\textwidth}}
\tabcellsep 2.3\\
\tabcellsep 2.2\\
\tabcellsep 2.1\\
\tabcellsep 2\\
\tabcellsep 1.9\\
\tabcellsep 1.8\\
\tabcellsep 1.7\\
Real\tabcellsep 1.6\\
\tabcellsep 1.5\\
\tabcellsep 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3\end{longtable} \par
 
\caption{\label{tab_5}dry density gm/cm3 Predicted dry density gm/cm3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.7666666666666666\textwidth}P{0.08333333333333333\textwidth}}
Year 2018\tabcellsep Figure 13:\\
34\tabcellsep \\
Global Journal of Researches in Engineering ( ) Volume XVIII Issue II Version I E\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}dry density gm/cm3 Predicted dry density gm/cm3}\end{figure}
 			\footnote{© 2018 Global Journals} 		 		\backmatter  			 \par
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\end{document}
