Scientifi c Review – Engineering and Environmental Sciences (2020), 29 (2), 234–243 Sci. Rev. Eng. Env. Sci. (2020), 29 (2) Przegląd Naukowy – Inżynieria i Kształtowanie Środowiska (2020), 29 (2), 234–243 Prz. Nauk. Inż. Kszt. Środ. (2020), 29 (2) http://iks.pn.sggw.pl DOI 10.22630/PNIKS.2020.29.2.20 Firas Kh. JABER1, Nidal A. JASIM2, Faiq M.S. Al-ZWAINY3 1 Middle Technical University, Electrical Engineering Technical College 2 University of Diyala, College of Engineering 3 Al-Nahrain University, College of Engineering Forecasting techniques in construction industry: earned value indicators and performance models Key words: Machine Learning Regression other industries by its many risks, and its Techniques (MLRT), earned value indexes, projects always suffer from the problems SPI, CPI, and TCPI of delay in implementation and increase in cost in most countries of the world. Among the most important characteris- Introduction tics of the construction industry (Myers, 2005): The construction industry is an im- 1. The nature of its product is unique, portant industry for any government each project differs from the other, due to its direct association with the im- and the temporary of each project is plementation of the goals and policies limited in duration and location, with of the government in various fi elds of the completion of the project, the concern to the citizen in terms of educa- equipment and labor will be trans- tion, health, housing and other facilities ferred to another project in another and services. The construction industry place. is also one of the broad and important 2. The nature of work within a single sectors of any country’s economy, and it project is fragmented, as several dif- is one of the main engines that govern- ferent parties separate and separate ments resort to move the economy and to complete the project. create jobs and reduce unemployment. 3. The industry’s heavy dependence on And the construction industry has unique manpower and its lack of research characteristics, which made it an industry and development in methods and that is distinguished from the rest of the methods of implementation. 234 F.Kh. Jaber, N.A. Jasim, F.M.S. Al-Zwainy 4.The criterion of competition for bid- cost status. Earned value management ding in most cases is the lower price, system in effect integrates time manage- which reduces the profi t margin and ment and cost management which are es- thus reduces the desire of the compa- sential elements of tall buildings projects nies executing in research, develop- management (Al-Zwainy, Mohammed & ment and creativity. Mohsen, 2015). The increasing demand for tall Review of the literature related to buildings represents a huge opportunity earned value management system with to reconstruct the construction industry Forecasting techniques such as Ma- and create value through joint action. chine Learning Regression Techniques According to research prepared by the (MLRT) was in top management jour- McKinsey Global Institute, the industry nals such as International Journal of can boost the productivity of its work- Project Management. Where, various ers by up to 60% if changes are made researchers have used Machine Learn- in seven key areas: regulations, design ing Regression Techniques (MLRT) as operations, contracts, supply and supply a tool for prediction and optimization chain management, and on-site imple- in different project management ar- mentation (Granskog, Guttman & Sjö- eas are also reviewed, including earned din, 2016). value management. The great majority Tall buildings since the 1990s and of project management applications of during the fi rst decade of the 21st cen- Multiple Linear Regression are based on tury are considered civilization and ur- the inter algorithm. The previous studies banization facilities, not only that, but are among the most important scientifi c tall buildings have become symbols of foundations on which the current study countries. Tall buildings are defi ned as is based. The researcher begins with re- those buildings whose height is not less search, examination and analysis in the than 40 m or 13 fl oors. In most cases, the previous studies; this is because the re- management of tall projects is not a sim- search processes are cumulative process- ple matter, as success in such projects es that depend on the previous ones. In requires good coordination as well as the next paragraph, the previous studies a comprehensive integrated operating on the earned value management will be system for the project, so that all project addressed and techniques used for fore- participants can understand their roles casting purposes. and agree on key performance indicators During the last few years or so, the (Al-Zwainy & Edan, 2017). use of EVM has increased in many con- Earned value management system struction engineering projects and has (EVMS) is a useful management tech- demonstrated some degree of success. nique available for tall buildings projects The following literature review reveals managers to monitor and control projects; that EVM have been used successfully in a technique that combines the work modelling of the earned value manage- scope, schedule and the cost elements ment. Pajares and Lopez-Paredes (2011) of a project and facilitates the integrated proposed two new metrics that combine reporting of a project’s progress and the earned value management and project Forecasting techniques in construction industry: earned value indicators... 235 risk management for construction project neurial orientation and entrepreneurial controlling and monitoring. Elshaer attitude of the individuals. (2013) studied the impact of the activi- The researchers did not found any ties sensitivity information on the fore- study that is exactly the same as the cur- casting accuracy of the Earned Schedule rent study while searching in interna- Method (ESM). Czemplik (2014) applied tional libraries and discreet periodicals schedule forecast Indicator (SFI) to sup- such as Scopus, Springer, Taylor France port site managerial decision concerning and others, as this current study is almost variation orders. Khamooshi and Golaf- unique of its kind that deals with predict- shani (2014) provided a new approach to ing the value indicators gained using decouple the schedule and cost dimen- Machine Learning Regression Tech- sions in EVM by adding earned duration niques (MLRT) in tall building projects, while maintaining the unique interaction despite the existence of multiple studies of the three major project management dealing with a topic prediction in con- elements of scope, cost, and time similar struction projects. Based on the above to EVM. Chen, Chen and Lin (2016) im- discussion the contribution of this study proved the predictive power of Planned are as follows. Value (PV) before executing construction Firstly, in this study, the main tar- project. Jaber, Hachem and Al-Zwainy get is on using associate Forecasting (2019) applied the value management techniques for the aim of earned value methodology acquired in the waste wa- prediction, meant as a decision-support ter treatment plant projects to measure aid to project manager, contractors and performance by predicting performance planners. The contribution of this study indicators such as CPI, SPI and TCPI. is to spot the most effective activity in The following literature review re- terms of model structure and parameters veals that Machine Learning Regression on an earned value prediction. Applied Techniques (MLRT) have been used suc- interest of this study may well be use cessfully in construction project manage- machine intelligence techniques such ment (Al-Zwainy et al., 2015) developing as Machine Learning Regression Tech- EAC model for bridges projects using niques (MLRT) as an acceptable tool for Multifactor Linear Regression Technique earned value estimating, where, Project (MLRT). Bilal and Oyedele (2020) pre- managers in Iraq; typically have to be sented guidelines for Applied Machine compelled to estimate the earned value Learning (AML) in the construction in- of construction projects at construction dustry from training to operationalizing stage quickly and around to supply fund- models, which are drawn from our ex- ing or to get the adoption of the budget perience of working with construction from decision-makers. Therefore, it’s folks to deliver Construction Simulation necessary to search out the simplest way Tool (CST). Sabahi and Parast (2020) to understand the earned value of the used predictive analytics by proposing a construction projects in a very short time machine learning approach to predict in- with acceptable accuracy. dividuals’ project performance based on In summary, the application EVM in measures of several aspects of entrepre- tall building projects would be specifi - 236 F.Kh. Jaber, N.A. Jasim, F.M.S. Al-Zwainy cally helpful for a state like Iraq which SPSS is short for Statistical Package suffers from shortage of a high number for the Social Sciences, and it’s utilized of severe tall buildings. The main ob- by different sorts of analysts for com- jective of this study is to develop Ma- plex measurable information investiga- chine Learning Regression Techniques tion. The SPSS programming bundle (MLRT) to predict the earned value in- was made for the administration and fac- dicators at early stage of tall buildings tual examination information in different projects in Iraq to reduce the percentage sciences. Long produced by SPSS Inc., error of estimation. This will facilitate it was acquired by IBM in 2009. The the project practitioners to the further use current versions are named IBM SPSS of EVA for the schedule and cost control Statistics. of their construction projects. To achieve this, there is a need to identify the factors that affect the performance of residential Identifi cation ANN Models buildings projects that can be available variables at early stages. Therefore, the specialists researchers in this study are endeavor- The Bismayah City Project is the fi rst ing improvement and assessment earned of its kind and the largest in the history value models through the below stages: of Iraq in terms of size and level of ser- 1.