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Building information modeling and building knowledge modeling in project management

Jasim Nidal A., Aljumaily Hadi S. M., Varouqa Ibrahim Farouq, Al-Zwainy Faiq M. S.

Computer Assisted Methods in Engineering and Science

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2021

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Vol. 28, no. 1

3--16

EN

Background: The key element for the success of construction projects in the era of tech-nology and information is knowledge. Knowledge management in the construction sectorhas become a necessary and vital matter, starting with the planning stage, designing,implementing and ending with operation and maintenance stage. One of the most promi-nent features of knowledge in the construction sector is the building knowledge modeling(BKM).Problem: Although the design stage is considered one of the most importantstages of the construction project in terms of eliciting the knowledge element, designers suf-fer from poor knowledge management, as well as their inability to exploit the capabilitiesand benefits of building information modeling (BIM), especially knowledge management.Objective: The main aim of this study is to present a conceptual model that integratesthe basic principles of knowledge management with contemporary principles of BIM inthe design stage and has the ability to store, classify and share the knowledge gener-ated from past designs into future projects.Methodology: The research methodologyis constructed on reviewing the pillars of the proposed conceptual model, namely: BKMparameters, BKM library, and BKM clash detection system, then a hypothetical case toprove the validity of the proposed conceptual model using Basmaya residential complexproject in the Republic of Iraq is presented as a case study.Results: New four definitions are introduced: client knowledge, context knowledge, standards knowledge and technicalknowledge. The conceptual model proposed in this study has shown great ability andhigh efficiency in improving and developing knowledge management in the design stage ofbuildings and projects. In addition, the knowledge stored in the BKM library can be reusedin designing new projects in the future.Novelty: The conceptual model has the abilityto update constantly itself over time by sharing the knowledge of designers, suppliers andother stakeholders.

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

Jaber Firas K., Jasim Nidal A., Al-Zwainy Faiq M.

Przegląd Naukowy Inżynieria i Kształtowanie Środowiska

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2020

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Vol. 29, No. 2

234--243

EN

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 coefficient (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.

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Predicting index to complete schedule performance indicator in highway projects using artificial neural network model

Jasim Nidal A., Maruf Shelan M., Aljumaily Hadi S. M., Al-Zwainy Faiq M. S.

Archives of Civil Engineering

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2020

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Vol. 66, nr 3

541--554

EN

Inaccurate estimation in highway projects represents a major problem facing planners and estimators, especially when data and information about the projects are not available, and therefore the need to use modern technologies that addresses the problem of inaccuracy of estimation arises. The current methods and techniques used to estimate earned value indexes in Iraq are weak and inefficient. In addition, there is a need to adopt new and advanced technologies to estimate earned value indexes that are fast, accurate and flexible to use. The main objective of this research is to use an advanced method known as artificial neural networks to estimate the TSPI of highway buildings. The application of artificial neural networks as a new digital technology in the construction industrial in Republic of Iraq is absolutely necessary to ensure successful project management. One model built to predict the TCSPI of highway projects. In this current study, artificial neural network model were used to model the process of estimating earned value indexes, and several cases related to the construction of artificial neural networks have been studied, including network architecture and internal factors and the extent of their impact on the performance of artificial neural network models. Easy equation was developed to calculate that TSPI. It was found that these networks have the ability to predict the TSPI of highway projects with a very outstanding saucepan of reliability (97.00%), and the accounting coefficients (R) (95.43%).