Search Results (17)

In today’s competitive business environment, digital transformation is crucial for organizational success. The Jordanian banking sector faces the challenges of adapting to rapid digital advancements, evolving customer expectations, and intense competition. This study investigated the impact of digital transformation capabilities—technological adaptation, strategic positioning, and competitive positioning—on perceived performance among 129 bank managers from 16 Jordanian commercial banks. Data were collected via a web-based survey that included a 29-item perceptual scale using a 5-point Likert scale. Multiple linear regression analysis revealed a significant positive relationship between these capabilities and perceived performance, explaining 68% of the variance. Specifically, technological adaptation (β = 0.310), strategic positioning (β = 0.260), and competitive positioning (β = 0.360) all significantly predicted perceived performance. Harman’s single-factor test indicated minimal common method bias, and strong positive correlations were found among all study variables. This research underscores the importance of a holistic digital transformation strategy for Jordanian banks, emphasizing the need for strategic investments in technology, competitive differentiation, and alignment with business objectives. Future research should explore additional factors such as organizational culture and regulatory frameworks and incorporate objective performance measures to provide a more comprehensive understanding of the impact of digital transformation. This study offers valuable insights for practitioners, policymakers, and researchers seeking to navigate digital disruption and foster business growth. Full article

This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. Traditional clash detection methods in Building Information Modeling (BIM) systems are predominantly reactive, identifying discrepancies only after their occurrence, leading to costly and time-consuming design revisions. By integrating ML algorithms with VR-driven BIM, our approach proactively identifies and resolves clashes, as demonstrated across 28 diverse engineering projects. The results indicate a reduction in design clashes by 16% and iterative revisions by 15%, culminating in a 12% decrease in overall project timelines. This research underscores the transformative impact of combining VR and ML on additive manufacturing (AM) workflows, significantly improving efficiency and reducing the iterative nature of traditional methods. The findings highlight the framework’s scalability and adaptability, promising substantial advancements in engineering and architecture practices. Full article

This research explores the application of digital twin technologies to progress the United Nations’ Sustainable Development Goals (SDGs) within the water-energy-food-environment (WEFE) nexus management in urban refugee areas. The study in Irbid Camp utilizes a detailed 3D Revit model combined with real-time data and community insights processed through advanced machine learning algorithms. An examination of 450 qualitative interviews indicates an 80% knowledge level of water conservation practices among the community but only 35% satisfaction with the current management of resources. Predictive analytics forecast a 25% increase in water scarcity and an 18% surge in energy demand within the next ten years, prompting the deployment of sustainable solutions such as solar energy installations and enhanced rainwater collection systems. By simulating resource allocation and environmental impacts, the digital twin framework helps in planning urban development in line with SDGs 6 (Clean Water and Sanitation), 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). This investigation highlights the capacity of digital twin technology to improve resource management, increase community resilience, and support sustainable urban growth, suggesting its wider implementation in comparable environments. Full article

Single-phasing is a common problem in the three-phase electrical grid. Despite the fact that the fault occurs in one phasing, the three-phase load is affected, and therefore the load is typically turned off. The three-phase induction motor is the most commonly used in the industry; therefore, this research investigates the behavior of the three-phase induction motor under a single-phasing fault. The main aim of this paper is to answer the question, should the three-phase induction motor be turned off under a single-phasing fault? The problem is investigated theoretically and compared with practical tests to explore the parameters of the induction motor (current, stator temperature, and vibration) that are affected under healthy and single-phasing fault conditions. A practical test machine is built to test the motor behavior under single-phasing faults, where the practical experiment results are compared to those of the simulations. Despite the common recommendation under single-phasing fault is to turn off the induction motors, the preliminary results of this study show that turning off an induction motor under single-phasing can be avoided under certain operating conditions with a simple protection scheme, which is useful in some practical situations. Full article

This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE). Full article

This study examines the impact of family ownership on tax avoidance decisions. This study further investigates the effects of corporate governance quality on the relationship between family ownership and tax avoidance. We construct a sample of non-financial firms listed on the ASE for the period 2015–2021. The results demonstrate that family-owned firms have high levels of tax avoidance. This result supports the private-benefit expropriation hypothesis. Regarding the mediating effect of corporate governance variables, the results suggest that large audit committees and audit committees that meet more frequently curb attempts by family owners to avoid paying tax. Full article

Addressing hunger, particularly within impoverished communities in Jordan and globally, demands innovative, practical solutions. The research focused on refugee populations and their preferences for interventions aligned with Sustainable Development Goal (SDG) 2: Zero Hunger remains limited. This study explores the preferences of refugees in the Irbid Camp and Sakhra region, Jordan, for two potential interventions—cultivated roofs ($CRs$) and refrigerators as food banks ($RaFB$). Surveys conducted among 402 households serve to determine refugee preferences in hunger reduction, the influence of demographic attributes on these choices, and the feasibility of each proposed intervention. Chi-square tests were utilized to establish correlations between refugee intervention preferences and demographic variables, such as age, gender, education level, and family size. The results reveal a strong preference (90%) for $RaFB$ over $CRs$ (10%). While no significant demographic influence was identified on the acceptance of $CRs$, a strong correlation was discovered between the education level and the acceptance of the $RaFB$ intervention. $RaFB$ was predominantly favored due to its lower implementation costs, reduced effort, lower risk, cultural compatibility, and demonstrated success in similar contexts. Conversely, highly educated refugees were more likely to reject $RaFB$, indicating potential influences from diverse cultural perspectives or access to alternate solutions. This study provides valuable insight into the potential advantages and challenges of implementing $CRs$ and $RaFB$ projects. It further underscores the need for policymakers to consider demographic factors and cultural nuances in future intervention designs to achieve SDG 2 more effectively. Full article

As a fundamental feature of green building cost forecasting, external support is crucial. However, minimal research efforts have been directed to developing practical models for determining the impact of external public and private support on green construction projects’ costs. To fill the gap, the current research aims to develop a mathematical model to explore the balance of supply and demand under deflationary conditions for external green construction support and the accompanying spending adjustment processes. The most current datasets from 3578 green projects across Northern America were collected, pre-processed, analyzed, post-processed, and evaluated via cutting-edge machine learning (ML) techniques to retrieve the deep parameters affecting the green construction cost prediction process. According to the findings, public and private investments in green construction are projected to decrease the cost of green buildings. Furthermore, the impact of public and private investment on green construction cost reduction during deflationary periods is more significant than its influence during inflation. As a result, decision-makers may utilize the suggested model to monitor and evaluate the yearly optimal external investment in green building construction. Full article

This work describes an experimental and machine learning approach for the prediction of selenite removal on chemically modified zeolite for water treatment. Breakthrough curves were constructed using iron-coated zeolite adsorbent and the adsorption behavior was evaluated as a function of an initial contaminant concentration as well as the ionic strength. An elevated selenium concentration in water threatens human health and aquatic life. The migration of this metalloid from the contaminated sites and the problems associated with its high releases into the water has become a major environmental concern. The mobility of this emerging metalloid in the contaminated water prompted the development of an efficient, cost-effective adsorbent for its removal. Selenite $\left[\mathrm{Se}\left(\mathrm{IV}\right)\right]$ removal from aqueous solutions was studied in laboratory-scale continuous and packed-bed adsorption columns using iron-coated natural zeolite adsorbents. The proposed adsorbent combines iron oxide and natural zeolite’s ability to bind contaminants. Breakthrough curves were initially obtained under variable experimental conditions, including the change in the initial concentration of $\mathrm{Se}\left(\mathrm{IV}\right)$, and the ionic strength of solutions. Investigating the effect of these parameters will enhance selenite mobility retardation in contaminated water. Continuous adsorption experiment findings will evaluate the efficiency of this economical and naturally-based adsorbent for selenite removal and fate in water. Multilinear and non-linear regressions approaches were utilized, yet low coefficients of determination values were respectively obtained. Then, a comparative analysis of five boosted regression tree algorithms for a selenite breakthrough curve prediction was performed. AdaBoost, Gradient boosting, $XGBoost$, $LightGBM$, and $CatBoost$ models were analyzed using the experimental data of the packed-bed columns. The performance of these models for the breakthrough curve prediction under different operation conditions, such as initial selenite concentration and ionic strength, was discussed. The applicability of these models was evaluated using performance metrics (i.e., Mean Absolute Error ($MAE$), Root Mean Square Error ($RMSE$), Mean Absolute Percentage Error ($MAPE$), and coefficient of determination ($R^2$). The $CatBoost$ model provided the best fit for a breakthrough prediction with a coefficient of determination $R^2$ equal to 99.57. The k-fold cross-validation technique and the statistical metrics verify this model’s accurateness. A feature importance assessment indicated that $\mathrm{Se}\left(\mathrm{IV}\right)$ initial concentration was the most influential experimental variable, while the ionic strength had the least effect. This finding was consistent with the column transport results, which observed $\mathrm{Se}\left(\mathrm{IV}\right)$ sorption dependency on its inlet concentration; simultaneously, the ionic strength effect was negligible. This work proposes implementing machine learning-based approaches for predicting water remediation-associated processes. The significance of this work was to provide an alternative method for investigating selenite adsorption behavior and predicting the breakthrough curves using a machine-based approach. This work also highlighted the importance of management practices of adsorption processes involved in water remediation. Full article

The influence of concrete mix properties on the shear strength of slender structured concrete beams without stirrups (SRCB-WS) is a widespread point of contention. Over the past six decades, the shear strength of SRCB-WS has been studied extensively in both experimental and theoretical contexts. The most recent version of the ACI 318-19 building code requirements updated the shear strength equation for SRCB-WS by factoring in the macroeconomic factors and the contribution of the longitudinal steel structural ratio. However, the updated equation still does not consider the effect of the shear span ratio ($a/d$) and the yield stress of longitudinal steel rebars ($F_y$). Therefore, this study investigates the importance of the most significant potential variables on the shear strength of SRCB-WS to help develop a gene expression-based model to estimate the shear strength of SRCB-WS. A database of 784 specimens was used from the literature for training and testing the proposed gene expression algorithm for forecasting the shear strength of SRCB-WS. The collected datasets are comprehensive, wherein all considered concrete properties were considered over the previous 68 years. The performance of the suggested algorithm versus the ACI 318-19 equation was statistically evaluated using various measures, such as root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. The evaluation results revealed the superior performance of the proposed model over the current ACI 318-19 equation. In addition, the proposed model is more comprehensive and considers additional variables, including the effect of the shear span ratio and the yield stress of longitudinal steel rebars. The developed model reflects the power of employing gene expression algorithms to design reinforced concrete elements with high accuracy. Full article

Highway construction projects are important for financial and social development in the United States. Such types of construction are usually accompanied by construction delay, causing liquidated damages ($LDs$) as a contractual provision are vital in construction agreements. Accurate quantification of $LDs$ is essential for contract parties to avoid legal disputes and unfair provisions due to the lack of appropriate documentation. This paper effort sought to develop an ensemble machine learning technique ($EMLT$) that combines algorithms of the Extreme Gradient Boosting ($XGBoost)$, Categorical Boosting ($CatBoost$), k-Nearest Neighbor ($kNN$), Light Gradient Boosting Machine ($LightGBM$), Artificial Neural Network ($ANN$), and Decision Tree ($DT$) for the prediction of $LDs$ in highway construction projects. Key attributes are identified and examined to predict the interrelated correlations among the influential features to develop accurate forecast models to assess the impact of each delay factor. Various machine-learning-based models were developed, where the different modeling outputs were analyzed and compared. Four performance matrices such as Root Mean Square Error ($RMSE$), Mean Absolute Error ($MAE$), Mean Absolute Percentage Error ($MAPE$), and Coefficient of Determination ($R^2$) were used to assess and evaluate the accuracy of the implemented machine learning ($ML$) algorithms. The prediction outputs implied that the developed EMLT model has shown better performance compared to other ML-based models, where it has the highest accuracy of 0.997, compared to the DT, kNN, CatBoost, XGBoost, LightGBM, and ANN with an accuracy of 0.989, 0.988, 0.986, 0.975, 0.873, and 0.689, respectively. Thus, the findings of this research designate that the EMLT model can be used as an effective administrative decision adding tool for forecasting the $LDs$. As a result, this paper emphasizes ML’s potential to aid in the advancement of computerization as a comprehensible subject of investigation within highway building projects. Full article

Pipelines are widely used to transport water, wastewater, and energy products. However, the recently published American Society of Civil Engineers report revealed that the USA drinking water infrastructure is deficient, where 12,000 miles of pipelines have deteriorated. This would require substantial financial investment to rebuild. Furthermore, the current pipeline design practice lacks the guideline to obtain the optimum steel reinforcement and pipeline geometry. Therefore, the current study aimed to fill this gap and help the pipeline designers and practitioners select the most economical reinforced concrete pipelines with optimum steel reinforcement while satisfying the shear stresses demand and serviceability limitations. Experimental testing is considered uneconomical and impractical for measuring the performance of pipelines under a high soil fill depth. Therefore, a parametric study was carried out for reinforced concrete pipes with various diameters buried under soil fill depths using a reliable finite element analysis to execute this investigation. The deflection range of the investigated reinforced concrete pipelines was between 0.5 to 13 mm. This indicates that the finite element analysis carefully selected the pipeline thickness, required flexural steel reinforcement, and concrete crack width while the pipeline does not undergo excessive deformation. This study revealed that the recommended optimum reinforced concrete pipeline diameter-to-thickness ratio, which is highly sensitive to the soil fill depth, is 6.0, 4.6, 4.2, and 3.8 for soil fill depths of 9.1, 12.2, 15.2, and 18.3 m, respectively. Moreover, the parametric study results offered an equation to estimate the optimum pipeline diameter-to-thickness ratio via a design example. The current research outcomes are imperative for decision-makers to accurately evaluate the structural performance of buried reinforced concrete pipelines. Full article

The rapid growth of using the short links in steel buildings due to their high shear strength and rotational capacity attracts the attention of structural engineers to investigate the performance of short links. However, insignificant attention has been oriented to efficiently developing a comprehensive model to forecast the shear strength of short links, which is expected to enhance the steel structures’ constructability. As machine learning algorithms was successfully used in various fields of structural engineering, the current study fills the gap in estimating the shear strength of short links using sophisticated machine learning algorithms. The deriving factors such as web and flange slenderness ratios, the flange-to-web area ratio, the forces in web and flange, and the link length ratio were investigated in this study, which is imperative to formulate an integrated prediction model. Consequently, the aim of this study utilizes advanced machine learning (ML) models (i.e., Extreme Gradient Boosting ($XGBOOST$), Light Gradient Boosting Machine ($LightGBM$), and Artificial Neural Network ($ANN$) to produce accurate forecasting for the shear strength. In this study, publicly available datasets were used for the training, testing, and validation. Different evaluation metrics were employed to evaluate the prediction’s performance of the used models, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²). The prediction result displays that the $XGBOOST$ and $LightGBM$ provided better, and more reliable results compared to $ANN$ and the AISC code. The $XGBOOST$ and $LightGBM$ models yielded higher values of R², lower (RMSE), (MAE), and (MAPE) values and have shown to perform more accurate. Therefore, the overall outcomes showed that the $LightGBM$ outperformed the $XGBOOST$ model. Moreover, the overstrength ratio predicted by the $LightGBM$ showed an excellent performance compared to the Gene Expression and Finite Element-based models. The developed models are vital for practitioners to predict the shear strength accurately, which pave the road towards wider application for automation in the steel buildings. Full article

Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience in contract cost estimation. Unlike conventional building construction, green buildings are designed to utilize new technologies to reduce their operations’ environmental and societal impacts. Consequently, green buildings’ construction bidding and awarding processes have become more complicated due to difficulties forecasting the initial construction costs and setting integrated selection criteria for the winning bidders. Thus, robust green building cost prediction modeling is essential to provide stakeholders with an initial construction cost benchmark to enhance decision-making. The current study presents machine learning-based algorithms, including extreme gradient boosting (XGBOOST), deep neural network (DNN), and random forest (RF), to predict green building costs. The proposed models are designed to consider the influence of soft and hard cost-related attributes. Evaluation metrics (i.e., MAE, MSE, MAPE, and $R^2$) are applied to evaluate and compare the developed algorithms’ accuracy. XGBOOST provided the highest accuracy of 0.96 compared to 0.91 for the DNN, followed by RF with an accuracy of 0.87. The proposed machine learning models can be utilized as a decision support tool for construction project managers and practitioners to advance automation as a coherent field of research within the green construction industry. Full article

Sustainable construction projects are essential for economic and societal thriving in modern communities. However, infrastructural construction is usually accompanied by delays in project delivery, which impact sustainability. Such delays adversely affect project time, cost, quality, safety objective functions, and associated Liquidated Damages (LDs). LDs are monetary charges to recompense the owner for additional expenses sustained if the project was not delivered on time due to delays caused by the contractor. This paper proposes modified regression modeling using machine learning (ML) techniques to develop solutions to the problem of predicting LDs for construction projects. The novel modeling methodology presented here is based on six years of data collection from many construction projects across the United States. It represents an innovative use of Multiple Linear Regression (MLR) models hybridized with machine learning (ML). The proposed methodology is evaluated using real datasets, where the developed model is designed to outperform the state-of-the-art LD forecast accuracy. Herein, seven modified regression-based models showed high accuracy in predicting the LDs. Nevertheless, those models’ forecasting ability was limited, so another second-order prediction model is proposed to provide better LD estimations. Independent variables were categorized based on their influence on the estimated LDs. The Total Bid Amount variable had the highest impact, while the Funding Indicator variable had a minimal impact. LD prediction was negatively correlated with all change-order-related variables and Total Adjustment Days, which suggests that those variables introduce extreme uncertainties due to their complex nature. The developed prediction models help decision-makers make better LDs predictions, which is essential for construction project sustainability. Full article