BIM Implementation Maturity Level and Proposed Approach for the Upgrade in Lithuania

Natalija; Rana; Sonia; Vaidotas

doi:https://doi.org/10.54216/IJBES.020102

International Journal of BIM and Engineering Science | 2019

Methodology for BIM implementation in the Kingdom of Saudi Arabia

Purpose – The Architecture, Engineering, and Construction (AEC) industry is considered the most effective contributor to development in the Kingdom of Saudi Arabia (KSA). However, the AEC industry is facing myriad challenges due to the vast construction development required for the KSA 2030 vision. Developed countries are using Building Information Modeling (BIM) to mitigate these challenges and reap the benefits of implementing BIM to improve the performance of the AEC industry profoundly. However, BIM is currently rarely used in the KSA. This study aims to develop a methodology to implement BIM in the KSA by exploring stakeholders’ perception of factors affecting the implementation. Design/methodology/approach – BIM users and non-users were surveyed by means of a questionnaire and structured interviews. The proposed methodology was validated through a further survey and structured interviews with BIM experts. Findings – This study proposes a six-step methodology to implement BIM namely; raising awareness; perceived benefits; AEC industry readiness, and organizations’ capability; identifying the barriers; removing the barriers; and defining the key factors influencing the implementation. Practical implications – The proposed methodology is expected to assist project participants in KSA to implement BIM to solve current AEC industry issues, improve projects’ performance and reap the benefits of implementing BIM. Originality/value – This study makes a crucial and novel contribution by providing a new methodology to implement BIM in KSA that motivates decision makers and project players to adopt and implement BIM in their projects. It paves the way to develop BIM guidance and strategies.

groups

Ashraf Elhendawi mail -

Andrew Smith mail -

Emad Elbeltagi mail

link https://doi.org/10.54216/IJBES.020101

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2018

BIM Performance Improvement Framework for Syrian AEC Companies

The Architectural, engineering, and construction (AEC) industry projects in Syria struggled with myriad problems. However, Building Information Modelling (BIM) technology worldwide proves its capability to solve these issues, Syrian AEC companies are rarely using BIM. Therefore, the aim of this study is to improve the BIM performance in Syrian AEC companies which are already in the BIM zero level and to provide strategies to the companies which do not use BIM for BIM adoption in their projects. An extensive literature review has been conducted to investigate the latest strategies and frameworks to implement and improve BIM performance. In addition to, an online questionnaire analysed by SPSS software and Excel to develop the suggested framework. Furthermore, the General Company for Engineering Studies and Consultations (GCEC) is selected as a case study to validate the framework. This study assessed and enabled the company to improve its BIM performance by using BIM maturity matrix (BIM3) through three stages: 1) Identified BIM and its performance, 2) Performance measurement, 3) Performance improvement. This study provides a new and novel companies’ BIM performance improvement framework which consisted of three fields: policy, process, and technology. The results of this study assisted to identify, obtain, and improve BIM interactions between individuals and companies to enhance the collaboration between all project participants. The future research will attempt to test and validate the proposed framework for private sector companies.

groups

Sonia Ahmed mail -

Petr DlasK mail -

Omar Selim mail -

Ashraf Elhendawi mail

link https://doi.org/10.54216/IJBES.010102

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2026

Intelligent Data Analysis of Asymmetric Oil Price Transmission and Financial Development: Evidence from an Emerging Market Economy

This article provides a data analysis framework to study asymmetric macro-financial relationships in an emerging market economy with significant energy dependence. Using annual observations for Egypt over 1990–2024, we estimate a nonlinear autoregressive distributed lag (NARDL) error-correction model in which changes in Brent crude prices are algorithmically decomposed into positive and negative cumulative partial-sum series. A composite financial development index, constructed from banking-sector depth indicators, enters the model both as a direct regressor and as an interaction term with each shock component. The results show that positive oil-price shocks carry a substantially larger long-run penalty on real GDP growth than negative shocks of equal magnitude—consistent with the cost-side exposure of a net oil-importing economy. Financial deepening conditions the transmission of these shocks but does not neutralise them; the allocation of credit toward productive private-sector activity, rather than the aggregate volume of intermediation, determines the direction of the moderating effect. Rolling-window and dynamic multiplier analyses confirm structural instability in the oil–growth relationship across sub-periods, validating the nonlinear modelling approach over standard linear alternatives. Unit root tests with structural breaks, NARDL bounds tests, and a battery of diagnostic checks support the robustness of the estimated long-run relationships. The findings carry direct implications for energy-risk management, financial-sector reform, and growth-stability policy in emerging market settings.

groups

Heba Moselhy mail -

Noura Metawa mail

link https://doi.org/10.54216/AJBOR.140206

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Metaheuristic Optimization Review | 2026

DTOSFS–CatBoost: A Hybrid Metaheuristic Framework for Accurate and Interpretable Unemployment Forecasting

The fact that educational, demographic, and macroeconomic variables interact nonlinearly has remained a thorn in the flesh of socio-economic analytics to date, making it challenging to forecast unemployment with sufficient precision. To address this, the current study presents a hybrid metaheuristic, Dipper Throated Optimization with Stochastic Fractal Search (DTOSFS), coupled with the Category Boosting (CatBoost) algorithm to improve predictive modelling. The suggested DTOSFS-CatBoost system combines the general exploratory search of DTO with SFS refinement to stochastic local optimization of hyperparameters, and alleviates overfitting. Empirical experiments have shown that whereas the original CatBoost gave results with a Mean Squared Error (MSE) of 0.0256 and Root Mean Squared Error (RMSE) of 0.1601 with a correlation coefficient of 0.873, the CatBoost optimized by DTOSFS had drastically better results with an MSE of 0.00033, RMSE of 0.00207, and a correlation coefficient of 0.930. These results confirm an increased exploration-to-exploitation ratio in DTOSFS and yield small, powerful designs that substantially enhance model stability, precision, and convergence speed. These results show that educational attainment (at least tertiary and primary enrollment) and demographics (at least the birth rate) are influential factors in unemployment variation. This addition to predictive performance is not the only one, and it provides a predictive data-driven labor-market optimization paradigm that can be replicated and interpreted. The research observes that hybrid metaheuristics and gradient boosting can be used to drive next generation economic intelligence systems for adaptive policy formulation and to enhance online, privacy conscious, and cross-domain unemployment prediction.

groups

Ghassan AL-Thabhawee mail -

Hussein Alkattan mail

link https://doi.org/10.54216/MOR.060101

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Optimizing Digital Marketing Revenue Forecasting Using an XGBoost–Dipper Throated Optimization Hybrid Model

The explosive growth of digital marketing data and the increasing need for accurate revenue forecasting have driven the adoption of advanced Machine Learning (ML) techniques capable of modeling complex, nonlinear relationships in dynamic environments. Motivated by the limitations of traditional linear forecasting methods, this study proposes an optimized predictive framework that integrates the Extreme Gradient Boosting (XGBoost) algorithm with a novel metaheuristic, Dipper Throated Optimization (DTO), to enhance model performance on temporal marketing data. The key contribution of this work lies in combining ensemble learning with bio-inspired optimization to achieve superior predictive accuracy and stability in Time-Series forecasting tasks. As the experiments of the Digital Marketing Metrics dataset demonstrate, the original XGBoost model achieved a Mean Squared Error (MSE) of 0.0905 and a coefficient of determination (R2) of 0.8007, and the optimized XGBoost+DTO model has significantly improved results, with an MSE of 0.0010 and a coefficient of determination (R2) of 0.9002. These results support the argument that DTO is effective in hyperparameter optimization and reducing generalization errors. The results of this paper are not unique to digital marketing, and the authors have presented a scalable, interpretable optimization model that can be generalized to other data intensive fields, such as financial analytics, demand forecasting, and customer behavior modelling. The study is a good step in the right direction of creating more accurate, adaptive and data-driven decision-making in the digital economy by integrating ML and nature-inspired optimization.

groups

Mohamed Rabehi mail -

Abdelaziz Rabehi mail

link https://doi.org/10.54216/MOR.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Enhanced Stock Price Forecasting: Time Series Analysis with ARIMA and FGGO Optimization

Forecasting financial markets remains a persistent challenge due to the nonlinear, stochastic, and nonstationary nature of stock price dynamics. This study is motivated by the need to enhance the robustness and adaptability of traditional statistical forecasting models through intelligent optimization. We propose an advanced hybrid framework that integrates the AutoRegressive Integrated Moving Average (ARIMA) model with the Fitness Greylag Goose Optimization (FGGO) algorithm—a refined metaheuristic inspired by collective behavioral intelligence and adaptive search strategies. The primary contribution of this research lies in the methodological fusion of classical time series modeling with dynamic metaheuristic optimization to improve predictive accuracy, convergence stability, and resistance to local optima. Comparative experiments on the historical stock prices of PT Bank Central Asia Tbk (BBCA.JK) demonstrate a substantial performance uplift: the baseline ARIMA model achieved a Mean Squared Error (MSE) of 0.0333, whereas the FGGO-optimized ARIMA reduced the MSE dramatically to 0.0038, outperforming other optimization techniques such as the Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). These results confirm that FGGO significantly enhances ARIMA’s capacity to capture intricate temporal dependencies and volatile market structures. The implications of this study extend beyond finance, offering a scalable, explainable, and high performance optimization paradigm for diverse time series forecasting applications in economics, engineering, and intelligent decision-support systems.

groups

Laith Farhan mail -

Raad S. Alhumaima mail

link https://doi.org/10.54216/MOR.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

Hybrid Metaheuristic–Ensemble Pipeline for Student Mental Health: Waterwheel Plant Algorithm with Random Forest

The problem of depression among college students has become a burning research problem, as the number of psychosocial stress factors, academic loads, and lifestyle disorders that lead to the worsening of mental health has increased. Driven by the increasing need for innovative, data-driven, and interpretable diagnostic models, this paper presents a combined Machine Learning (ML) and metaheuristic optimization model for predicting depression using multidimensional psychosocial and academic data collected from 100 Computer Science students. The suggested hybrid model combines a Random Forest (RF) classifier with the Waterwheel Plant Algorithm (WWPA), a nature-inspired mechanism for optimizing hyperparameter settings and feature selection. Experimentation using the Random Forest baseline model yielded a baseline accuracy of 0.9081, a Sensitivity (True Positive Rate) of 0.8936, and an F-Score of 0.9032. The hybrid WWPA+Random Forest model showed significant gains after introducing the WWPA optimization method, achieving a high accuracy of 0.9577, a sensitivity of 0.9502, a specificity of 0.9644, and an F-score of 0.9553. These findings validate the high-quality performance of the proposed model in achieving balanced, high-precision classification and in resisting overfitting. The results highlight the potential to integrate ensemble learning with bio-inspired optimization to advance depression prediction, providing a scalable, explainable, and ethically appropriate framework for predicting depression early in a person’s life. This work opens the way to creating a proactive digital mental health system that will enable educational organizations to identify at-risk students early and offer timely, individualized support for well-being and academic achievement.

groups

Amel Ali Alhussan mail -

Abdelaziz A. Abdelhamid mail

link https://doi.org/10.54216/MOR.060104

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2026

A Comparative Deep Learning Approach for Short-Term Wind Power Generation Prediction

Accurate wind power forecasting is essential for reliable renewable energy integration, grid stability, reserve scheduling, and wind farm operation because turbine output is highly variable and strongly influenced by meteorological conditions. However, forecasting wind power remains challenging due to the nonlinear relationship between weather variables and power generation, the temporal dependency of hourly observations, and the circular nature of wind direction data. This study aims to develop and compare deep learning models for predicting normalized wind turbine power output using a field-based hourly dataset collected from an operational wind energy site starting from January 2, 2017. The dataset includes temperature, relative humidity, dew point, wind speed at 10 m and 100 m, wind direction at 10 m and 100 m, wind gusts, and normalized turbine output. Five predictive models, namely LSTM, RNN, GRU, CNN, and Dense neural networks, were trained and evaluated after applying data preprocessing procedures, including missing-value handling, feature scaling, temporal alignment, and wind-direction transformation. Model performance was assessed using MSE, RMSE, MAE, MBE, correlation coefficient (r), coefficient of determination (R2), RRMSE, NSE, and WI. The empirical results showed that recurrent architectures outperformed the CNN and Dense models, confirming the importance of temporal learning in hourly wind power forecasting. Among all models, LSTM achieved the best overall performance, with MSE = 0.0008, RMSE = 0.0282, MAE = 0.0106, MBE = -0.0006, r = 0.9940, R2 = 0.9880, RRMSE = 0.0861, NSE = 0.9880, and WI = 0.9970. These findings demonstrate that LSTM can effectively capture nonlinear and sequential relationships between meteorological variables and turbine power generation, providing a reliable forecasting approach for operational wind energy management and supporting more stable integration of wind power into modern electricity systems.

groups

Mona Ahmed Yassen mail -

Mohamed G. Abdelfattah mail -

Islam Ismail mail -

Hossam El-Din Moustafa mail

link https://doi.org/10.54216/MOR.060105

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2018

Building Information Modeling in Syria: Obstacles and Requirements for Implementation

The crucial need for innovative sophisticated, and complex Architectural, engineering, and construction (AEC) industry projects with in-depth details makes traditional methods inappropriate for the completion of projects with desired efficiency, performance and productivity. Therefore, AEC projects in Syria suffered from myriad issues such as Behind the Schedule, over budget, inferior quality, low productivity, without sustainability and more. However, Building information Modelling (BIM) proves its capability to solve these issues. The aim of this study is to identify the obstacles and the critical influencing factors for applying BIM in Syria in the AEC industry. Extensive investigation for literature review and structured online questionnaire designed to achieve the study’s aim. SPSS and Excel were used to analyze the results. This study classified the obstacles related to three category: 1) Planning, Design and Auditing systems, 2) BIM System, 3) Management, Financial and Legal factors. In spite of, the government and clients play the vital role to mandate BIM, the mixed approach (top-down and bottom-up) is recommended to expedite BIM implementation. This study provides a novel contribution by identifying the main obstacles and developing new strategies for applying BIM in AEC and reconstruction which enhance projects quality, performance and efficiency.

groups

Mohamed H. Shaban mail -

Ashraf Elhendawi mail

link https://doi.org/10.54216/IJBES.010103

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2019

BIM Implementation Maturity Level and Proposed Approach for the Upgrade in Lithuania

Recently, Building information modelling (BIM) proves its capability to solve the raised AEC industry issues. Therefore, several countries and entities pursue to transform into BIM especially the developed countries. Lithuania as a European country has a great challenge to cap up with the surrounding environment to implement BIM. This study aims to determine the BIM maturity levels in Lithuania and supposed the missed steps to upgrade to the next level. Eighteen important Lithuanian construction projects awarded the most successful implementing BIM are chosen as a case study. Face-to-face interviews were conducted with several BIM experts whose work at the chosen projects. The analysis conducted by the most effective theoretical model entitled BIM Maturity Matrix (BIMM). The key findings of this research that Lithuania reached the BIM implementing maturity level 2 while some projects still at level 1 that proves the ability of Lithuanian AEC industry to softly and completely transfer the maturity to level 2 by the recommendation provided through the proposed approach at the end of the paper. These results provide a stunning opportunity to improve the AEC project performance and reap the benefits of implementing BIM. Future studies can develop a framework to improve the BIM implementation in Lithuania softly.

groups

Natalija Lepkova mail -

Rana Maya mail -

Sonia Ahmed mail -

Vaidotas Šarka mail

link https://doi.org/10.54216/IJBES.020102

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new