An Artificial neural network model for risk impacts on cash flow forecast in construction industry Key Words: Risk Factors, Risk Impacts, Model, Artificial Neural Network, Cash Flow Forecast Area of Research Cash flow forecasting is a vital contributing factor in construction industry where lead to the high rate of insolvencies. Risks involved with construction industry play significant role for the variation of forecasted and actual cash flow. Identification of risks and risk assessment are important to develop accurate cash flow forecast. Statistical, mathematical and simulation approaches were adapted to the identification of risk impact assessment. Though significant variation is still observed with actual and forecasted cash flow. So my Phd is focused in developing an artificial neural network model to assess the impact to cash flow forecast at the in progress stage of construction. Research Topic Prior studies were done to develop models to shortcut approach to cash flow forecasting based on project financial data and construction duration referred to as cost profile method (Kenley,2003). Three approaches were used in developing cost profile method; net cash flow, contract value data (value flow) and construction cost data (cost flow or cost commitment) by utilizing the three sides of the cash flow equation. Though significant deviation observed with actual and modeled forecast due to lack of addressing the risks impact inherent with construction industry. So in this…
The Kriging weights w_i1,w_i2,w_i3,…w_ik can be used to estimate the fair value of VA contracts x ⃑_i by the following formula, y ̂_i=∑_(j=1)^k▒〖w_ij∙y_j 〗. The kriging weights can be calculated by the following linear equations, Where the is a control variable, which is used to make sure that ∑_(j=1)^k▒w_ij =1. V_rs=α+exp(-3/β D(z ⃑_r,z ⃑_s,λ)),r,s=1,2,3,…,k, D_ij=α+exp(-3/β D(x ⃑_i,z ⃑_j,λ)),j=1,2,3,…,k, The D(.) function is the distance function mentioned in the clustering section,α≥0…
Problem: Apparatus 1: what is the relationship between height of release and the initial velocity? Apparatus 2: What is the relationship between length of the ball catcher and the final velocity of the system? Additional question: What is the percentage error of the initial velocity calculated using momentum and kinematics? Percentage error between theoretical initial velocity and experimental initial velocity? How much energy is lost? note: the initial velocity calculated using kinematics…
Weight Estimation using AHP In this section weight estimation using AHP is provided, where first dependency matrix is created based on saaty’s scale. Referring to matrix A1, every attribute is compared with others, ex. DT (Distance) first compared with itself so value is 1, then DT is compared with TT (Travel Time) as in this case TT is moderately important than DT is value will be 1/3, when DT is compared with PCU (Traffic Volume), in this case DT is moderately important than…
industry, thereby businesses can get high profit margins in the long-term. 3.4. Model and methodology Previous studies often used cross-sectional data or time-series data to build model. In this study, panel data is used, which allows for the use of panel data regression model. The analysis based on cross-sectional data model can only reflect the relationship between the variables in one year and is susceptible to accidental factor. Moreover, we can only analyze one unit over several time…
T.T.M Kannan et al. [2014] performed the experiment on AISI 316 Austenite stainless steel to investigate the heat partition, tool wear and tool life. In their investigation they found that CBN cutting inserts has been damaged in moderate cutting velocity and produce good machinability and higher cutting temperature decreases the yield strength of produced white layer. [11] R.Suresh et al. [2014] studied the effect of various cutting parameters in hard turning of AISI H13 steel at 55 HRC with…
Correlation analysis is used to determine the degree of relationship between variables. In correlation it is assumed that the variables mutually influence each other (Sharma, 2005). Baker and Saltes (2005) used a correlation to test capability of ABI for forecasting CS sectors and then used regression analysis’s coefficient of determination (R2) to indicate the proportion of the variance of CS that is explained by ABI. They found that non-residential CS is highly correlated with ABI in lag 5 for…
A0130036Y Method 1: Box Plot The question asks if Road Handling is part and parcel of Practicality, i.e. whether road handling is an essential component influencing/affecting another data group, practicality. Thus, road handling is taken to be the independent variable, x, and is ranked from the lowest to the highest score. The data is split into 5 groups, Group 1 and 5 containing the lowest-ranked and highest-ranked road handling scores, respectively. Group 1 2 3 4 5 Median 4.58 4.585 4.455…
It is worth mentioning that prediction of the concrete compressive strength is an important fact in the quality assurance of the produced concrete. Although there are numerous methods of predicting the mechanical properties of concrete, not all of them are valuable since they are in accordance with many trial and errors. As mentioned earlier, three different models of multiple linear regressing (MLR), artificial neural network (ANN), and ANFIS are used to reach the goal in this study. These…
The survey instrument created for this study includes relational questions to discover whether one or more working conditions predicts teacher retention and whether the absence of one or more working conditions predicts the likelihood a teacher will leave teaching or migrate to a new building. The survey contains both Likert-type scaled response ratings and demographic questions designed to identify teacher characteristics such as grade level. There are four constructs in this study:…