Forecasting techniques in construction industry: earned value indicators and performance models

Abstract

<jats:p>Machine Learning Regression Techniques (MLRT) as a shrewd method can be utilized in this study being exceptionally fruitful in demonstrating non-linear and the interrelationships among them in problems of construction projects such as the earned value indexes for tall buildings projects in Republic of Iraq. Three forecasting models were developed to foresee Schedule Performance Index (SPI) as first model, Cost Performance Index (CPI) as a second model, and the third model is To Complete Cost Performance Indicator (TCPI) in Bismayah New City was chosen as a case study. The methodology is mainly impacted by the deciding various components (variables) which impact on the earned value analysis, six free factors (X1: BAC, Budget at Completion; X2: AC, Actual Cost; X3: A%, Actual Percentage; X4: EV, Earned Value; X5: P%, Planning Percentage, and X6: PV, Planning Value) were self-assertively assigned and agreeably depicted for per tall buildings projects. It was found that the MLRT showed good results of estimation in terms of correlation coeffi cient (R) generated by MLR models for SPI and CPI and TCPI where the R were 85.5%, 89.2%, and 86.3% respectively. At long last, a result tends to be presumed that these models show a brilliant concurrence with the genuine estimations.</jats:p>

A Guideline of Earned Value Method (EVM) Implementation as Decision Baseline on Acceleration Solutions for Construction Project Scheduling

“…The ACWP is defined as the real cost of performed activity during a given time. This cost is the actual cost spent by the contractor and is sometimes confidential [18].…”

“…𝑐𝑟𝑎𝑠ℎ 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑚𝑎𝑛𝑝𝑜𝑤𝑒𝑟 𝑝𝑒𝑟 𝑑𝑎𝑦 = (𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑤𝑜𝑟𝑘ℎ𝑜𝑢𝑟 𝑝𝑒𝑟 𝑑𝑎𝑦 × 𝑛𝑜𝑟𝑚𝑎𝑙 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑚𝑎𝑛𝑝𝑜𝑤𝑒𝑟) + ( 𝑛 × 𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 𝑐𝑜𝑠𝑡 𝑝𝑒𝑟 ℎ𝑜𝑢𝑟) (17) 𝑐𝑜𝑠𝑡 𝑠𝑙𝑜𝑝𝑒 = 𝑐𝑟𝑎𝑠ℎ 𝑐𝑜𝑠𝑡 × 𝑛𝑜𝑟𝑚𝑎𝑙 𝑐𝑜𝑠𝑡 𝑛𝑜𝑟𝑚𝑎𝑙 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 (𝑖𝑛 𝑑𝑎𝑦𝑠)−𝑐𝑟𝑎𝑠ℎ 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛(18) …”

Archives of Civil Engineering

“…Firas et al in 2020 used forecasting techniques such as ANN to calculate the earned value indicators and performance of tall building. [16] The main objective of this study is to develop a mathematical model for artificial neural networks in order to predict the most important indicator of the earned value indexes in highway project, which is the To Complete Schedule Performance Indicator (TCSPI), while identifying the factors affecting it, as well as finding a mathematical equation to calculate the time index easily and simply.…”