Saja Hadi Raheem Aldhamad
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Saja Hadi Raheem Aldhamad has a total of 15 co-authors. Here are the top co-authors by number of shared publications:
- Faiq Mohammed Sarhan Al Zwainy
Forensic DNA Center for Research and Training, Al-Nahrain University, Jadriya, Baghdad, Iraq
2 shared publications - Hamza Imran
Department of Environmental Science, College of Energy and Environmental Science, Alkarkh University of Science, Baghdad 10081, Iraq
2 shared publications - Luís Filipe Almeida Bernardo
GeoBioTec, Department of Civil Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal
2 shared publications - Mohammed Ali Ahmed
Construction and Building Engineering Technologies Department, Najaf Engineering Technical College, Al-Furat-Al-Awsat Technical University, Najaf 54003, Iraq
2 shared publications - Suha Falih Mahdi Alazawy
Department of Highway and Airport Engineering, College of Engineering, University of Diyala, Ba’aqubah, Diyala, Iraq
2 shared publications - Aseel H. Obaid
Ministry of Education, General Directorate of Education in Baghdad Governorate, Rusafa II, Iraq
1 shared publication
Latest Publications
- Open Access
Explainable Machine Learning-Based Estimation of Labor Productivity in Rebar-Fixing Tasks
Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an of 0.901 and RMSE of 19.94 on the training set and an of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments.Open AccessExplainable Machine Learning-Based Estimation of Labor Productivity in Rebar-Fixing Tasks
Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an of 0.901 and RMSE of 19.94 on the training set and an of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. - Open Access
Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method
This study aims to develop a reliable method for predicting power plant construction costs during the early planning stages using ensemble machine learning techniques. Accurate cost predictions are essential for project feasibility, and this research highlights the strength of ensemble methods in improving prediction accuracy by combining the advantages of multiple models, offering a significant improvement over traditional approaches. This investigation employed the Random Forest (RF) algorithm to estimate the overall construction cost of a power plant. The RF algorithm was contrasted with single-learner machine learning models: Support Vector Regression (SVR) and k-Nearest Neighbors (KNN). Performance measures, comprising the coefficient of determination (), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), were used to evaluate and contrast the performance of the implemented models. Statistical measures demonstrated that the RF approach surpassed alternative models, demonstrating the highest coefficient of determination for testing () and the lowest Root Mean Square Error (RMSE = 29.27) for the testing dataset. The Shapley Additive Explanation (SHAP) technique was implemented to explain the significance and impact of predictor factors affecting power plant construction costs. The outcomes of this investigation provide crucial information for project decision-makers, allowing them to reduce discrepancies in projected costs and make informed decisions at the beginning of the construction phase.Open AccessExplainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method
This study aims to develop a reliable method for predicting power plant construction costs during the early planning stages using ensemble machine learning techniques. Accurate cost predictions are essential for project feasibility, and this research highlights the strength of ensemble methods in improving prediction accuracy by combining the advantages of multiple models, offering a significant improvement over traditional approaches. This investigation employed the Random Forest (RF) algorithm to estimate the overall construction cost of a power plant. The RF algorithm was contrasted with single-learner machine learning models: Support Vector Regression (SVR) and k-Nearest Neighbors (KNN). Performance measures, comprising the coefficient of determination (), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), were used to evaluate and contrast the performance of the implemented models. Statistical measures demonstrated that the RF approach surpassed alternative models, demonstrating the highest coefficient of determination for testing () and the lowest Root Mean Square Error (RMSE = 29.27) for the testing dataset. The Shapley Additive Explanation (SHAP) technique was implemented to explain the significance and impact of predictor factors affecting power plant construction costs. The outcomes of this investigation provide crucial information for project decision-makers, allowing them to reduce discrepancies in projected costs and make informed decisions at the beginning of the construction phase. - Open Access
- 3 April 2025
- Published by Learning Gate in Edelweiss Applied Science and Technology
- Vol. 9 (4) , 2432-2438
Significant applications of digital modeling in the construction sector for improving project management and performance
The purpose of the digital models is to aid users in better understanding the response and behavior of the construction management system under different conditions. The main advantage of the digital models lies in the ability to use possible alternatives and analyze the effect of various factors on the project management system, both before and during performance. This study will review previous works. Types of BIM and simulation techniques are discussed, along with the reasons for using this technique, its applications, and characteristics in the construction sector. The application of digital models in the construction sector is also reviewed. The research concludes that simulation modeling has a revolutionary impact on construction project management, as it provides advanced tools for planning efficiency, resource allocation, cost calculation, sustainability, and risk management. While obstacles such as the requirement for skilled personnel and the integration with existing systems remain, the benefits of simulation modeling in terms of cost savings, improved efficiency, and more effective decision-making make it an essential aid for construction projects.
- 3 April 2025
- Published by Learning Gate in Edelweiss Applied Science and Technology
- Vol. 9 (4) , 2432-2438
Open AccessSignificant applications of digital modeling in the construction sector for improving project management and performance
The purpose of the digital models is to aid users in better understanding the response and behavior of the construction management system under different conditions. The main advantage of the digital models lies in the ability to use possible alternatives and analyze the effect of various factors on the project management system, both before and during performance. This study will review previous works. Types of BIM and simulation techniques are discussed, along with the reasons for using this technique, its applications, and characteristics in the construction sector. The application of digital models in the construction sector is also reviewed. The research concludes that simulation modeling has a revolutionary impact on construction project management, as it provides advanced tools for planning efficiency, resource allocation, cost calculation, sustainability, and risk management. While obstacles such as the requirement for skilled personnel and the integration with existing systems remain, the benefits of simulation modeling in terms of cost savings, improved efficiency, and more effective decision-making make it an essential aid for construction projects.
- Open Access
- 2 December 2024
- Published by Engineering, Technology & Applied Science Research in Engineering, Technology & Applied Science Research
- Vol. 14 (6) , 17717-17724
An Efficient Novel Model for Multi-Story Building Construction Quantity Estimation using Coupled MATLAB-Revit Software
The nature of the construction business and building projects necessitates the capacity to manage extensive and intricate data and documentation. The processes for documenting, exchanging, and updating data constitute one of the principal administrative challenges being currently confronted by the construction project management. As a consequence of the continued reliance on paper-based processes evidenced in the Iraqi businesses and construction projects, a considerable volume of documentation is likely to accumulate, thereby increasing the complexity and time required for specific data to be retrieved. In this study, the Support Vector Machine (SVM) and Building Information Modeling (BIM) models were used to document projects by employing the MATLAB-Revit software. The findings demonstrate that the project timeline is also recorded because it is related to the suggested model, which is designed to produce an effective model that mimics reality. A comparative analysis of the data pertaining to the foundations, columns, walls (24 cm and 12 cm), floors, and slabs of four multi-story buildings, was conducted. This analysis was divided into three categories: estimated, SVM-BIM, and actual documentation. The findings indicated that the proposed model demonstrated a high degree of accuracy in predicting the material quantities required in building construction. These values were found to be in close proximity to, and aligned with, the actual documentation.
- 2 December 2024
- Published by Engineering, Technology & Applied Science Research in Engineering, Technology & Applied Science Research
- Vol. 14 (6) , 17717-17724
Open AccessAn Efficient Novel Model for Multi-Story Building Construction Quantity Estimation using Coupled MATLAB-Revit Software
The nature of the construction business and building projects necessitates the capacity to manage extensive and intricate data and documentation. The processes for documenting, exchanging, and updating data constitute one of the principal administrative challenges being currently confronted by the construction project management. As a consequence of the continued reliance on paper-based processes evidenced in the Iraqi businesses and construction projects, a considerable volume of documentation is likely to accumulate, thereby increasing the complexity and time required for specific data to be retrieved. In this study, the Support Vector Machine (SVM) and Building Information Modeling (BIM) models were used to document projects by employing the MATLAB-Revit software. The findings demonstrate that the project timeline is also recorded because it is related to the suggested model, which is designed to produce an effective model that mimics reality. A comparative analysis of the data pertaining to the foundations, columns, walls (24 cm and 12 cm), floors, and slabs of four multi-story buildings, was conducted. This analysis was divided into three categories: estimated, SVM-BIM, and actual documentation. The findings indicated that the proposed model demonstrated a high degree of accuracy in predicting the material quantities required in building construction. These values were found to be in close proximity to, and aligned with, the actual documentation.
- Open Access
- 1 December 2024
- Published by Walter de Gruyter GmbH in Civil and Environmental Engineering
- Vol. 20 (2) , 1024-1039
Forecasting Models for Time and Cost Performance Predicting of Infrastructural Projects
Developing artificial forecasting models to predict the cost index and time index is the primary objective of this research. The efficacy of residential property investment projects will be assessed through the implementation of models that incorporate Artificial Neural Networks and Multiple Linear Regression. Historical information of thirteen boundaries for twenty finished Private Property Venture Tasks were separated from the records of the Directorate of Lodging, then four models were created by utilizing Multiple Linear Regression strategy and Artificial Neural Networks method. The main model was Time Index model with test information that gave average accuracy equivalent to 78.48 % and delivered a high connection coefficient (R) equivalent to 95.3 % utilizing (MLR), the subsequent model was (CI) model with test information gave (AA) equivalents to 66.6 % and created (R) equivalents to 85.4 % utilizing (MLR), and the third model was (TI) model with test information gave (AA) equivalents to 90.31 %, and delivered a high (R) equivalents to 95.4 % utilizing (ANN), the fourth model was (CI) model with test information gave (AA) equivalents to 90.21 % and delivered a high (R) equivalents to 99.6 % utilizing (ANN). It was found that the NN model showed more exact assessment results than the MLR models. Thus, it was resolved that the NN model is generally appropriate for anticipating the time execution and cost execution of Residential Property Investment Projects.
- 1 December 2024
- Published by Walter de Gruyter GmbH in Civil and Environmental Engineering
- Vol. 20 (2) , 1024-1039
Open AccessForecasting Models for Time and Cost Performance Predicting of Infrastructural Projects
Developing artificial forecasting models to predict the cost index and time index is the primary objective of this research. The efficacy of residential property investment projects will be assessed through the implementation of models that incorporate Artificial Neural Networks and Multiple Linear Regression. Historical information of thirteen boundaries for twenty finished Private Property Venture Tasks were separated from the records of the Directorate of Lodging, then four models were created by utilizing Multiple Linear Regression strategy and Artificial Neural Networks method. The main model was Time Index model with test information that gave average accuracy equivalent to 78.48 % and delivered a high connection coefficient (R) equivalent to 95.3 % utilizing (MLR), the subsequent model was (CI) model with test information gave (AA) equivalents to 66.6 % and created (R) equivalents to 85.4 % utilizing (MLR), and the third model was (TI) model with test information gave (AA) equivalents to 90.31 %, and delivered a high (R) equivalents to 95.4 % utilizing (ANN), the fourth model was (CI) model with test information gave (AA) equivalents to 90.21 % and delivered a high (R) equivalents to 99.6 % utilizing (ANN). It was found that the NN model showed more exact assessment results than the MLR models. Thus, it was resolved that the NN model is generally appropriate for anticipating the time execution and cost execution of Residential Property Investment Projects.