Digital Twin Technology (DTT) has gained significant attention as a vital technology for the efficient management of smart cities. However, its successful implementation in developing countries is often hindered by several barriers. Despite limited research available on smart city development in Malaysia, there is a need to investigate the possible challenges that could affect the effective implementation of DTT in the country. This study employs a mixed methodology research design, comprising an interview, a pilot survey, and the main survey. Firstly, we identified barriers reported in the literature and excluded insignificant factors through interviews. Next, we conducted an Exploratory Factor Analysis (EFA) on the pilot survey results to further refine the factors. Finally, we performed a Structural Equation Modeling (SEM) analysis on the main survey data to develop a model that identifies barriers to DTT implementation in smart city development in Malaysia. Our findings suggest the presence of 13 highly significant barriers, which are divided into four formative constructs. We found that personalization barriers are highly crucial, while operational barriers were less important for DTT implementation in smart city development in Malaysia.

Scouring is a major concern affecting the overall stability and safety of a bridge. The current research investigated the effectiveness of the various artificial intelligence (AI) techniques, such as artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF), for scouring depth prediction around a bridge abutment. This study attempted to make a comparative analysis between these AI models and empirical equations developed by various researchers. The current research paper utilized a dataset of water depth (Y), flow velocity (V), discharge (Q), and sediment particle diameter (d50) from a controlled laboratory setting. An efficient optimization tool (MATLAB Optimization Tool (version R2023a)) was used to develop a scour estimation formula around bridge abutments. The findings of the current investigation demonstrated the superior performance of the AI models, especially the ANFIS model, over empirical equations by precisely capturing the non-linear and complex interactions between these parameters. Moreover, the result of the sensitivity analysis demonstrated flow velocity and discharge to be the most influencing parameters affecting the scouring depth around a bridge abutment. The results of the current research highlight the precise and accurate prediction of the scouring depth around a bridge abutment using AI models. However, the empirical equation (Equation 2) demonstrated better performance with a higher R-value of 0.90 and a lower MSE value of 0.0012 compared to other empirical equations. The findings revealed that ANFIS, when combined with neural networks and fuzzy logic systems, produced highly accurate and precise results compared to the ANN models.

Flooding is the most common natural disaster throughout the world and requires efficient management. Therefore, the current investigation aimed to explore the impact of a composite defense system comprising dyke and vegetation on flow dynamics and velocity reduction. Experiments were conducted in an open channel setup with an adjustable bed slope and transparent sidewalls, and the vegetation model was replicated as real trees such as Eucalyptus trees. The study involved calculating several parameters, including flow velocity, reduction of fluid force index (RFI%), reduction in moment index (RMI%), and hydrostatic and hydrodynamic forces. These calculations were done by changing the channel bed slope and keeping the flow rate (discharge) constant while considering both subcritical and supercritical flow conditions. Moreover, regression analysis was performed for the prediction of RFI% and RMI% under various flow conditions. Also, statistical analyses were performed to assess the effectiveness of the defense system in reducing fluid force and moment indices. The result of the current investigation indicates that the highest values of RFI% and RMI% under subcritical flow conditions were 79% and 88%, while under supercritical flow conditions they were 94% and 78%, respectively. Moreover, a velocity reduction of 69% was observed under subcritical flow, while 84% was observed under supercritical flow conditions. Under subcritical flow conditions, RFI% and RMI% enhanced by enhancing Froude number (Fr) because of an increase in velocity reduction and hydraulic jump formation. Similar trends were observed under supercritical flow conditions, with effective mitigation of high-velocity flows by the composite system. The finding of current research helps in providing effective techniques for flood management.

Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learning (ML) model, to categorize and predict work zone-related crashes and to interpret the various contributing factors. The issue of data imbalance was also addressed by utilizing work zone crash data from the state of New Jersey, comprising data collected over the course of two years (2017 and 2018) and applying data augmentation strategies such synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, and SVM-SMOTE. The EBM model was trained using augmented data and Bayesian optimization for hyperparameter tuning. The performance of the EBM model was evaluated and compared to black-box ML models such as combined kernel and tree boosting (KTBoost, python 3.7.1 and KTboost package version 0.2.2), light gradient boosting machine (LightGBM version 3.2.1), and extreme gradient boosting (XGBoost version 1.7.6). The EBM model, using borderline-SMOTE-treated data, demonstrated greater efficacy with respect to precision (81.37%), recall (82.53%), geometric mean (75.39%), and Matthews correlation coefficient (0.43). The EBM model also allows for an in-depth evaluation of single and pairwise factor interactions in predicting work zone-related crash severity. It examines both global and local perspectives, and assists in assessing the influence of various factors.

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM_2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R² of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM_2.5 concentrations, followed by humidity and temperature. Elevated PM_2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM_2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM_2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management.

Fine particulate matter (PM_2.5) poses major public health and environmental threats due to its capacity to enter deep respiratory passages and degrade urban air quality. In the Kingdom of Saudi Arabia (KSA), cities such as Riyadh, Dammam, and Jeddah show an elevated level of PM_2.5 due to rapid urban growth, dense traffic activity, and wide industrial operations. This study proposes a hybrid Variational Mode Decomposition–Bidirectional Gated Recurrent Unit (VMD–BiGRU) framework for multi-horizon PM_2.5 forecasts based on daily data from January 2022 to September 2024. The daily PM_2.5 series was split through VMD into Intrinsic Mode Functions (IMFs) that represent multi-scale temporal patterns. A seven-day ahead forecast was carried out, and model performance was compared with VMD–GRU, VMD–LSTM, and VMD–TCN. For Riyadh, RMSE values for t + 1, t + 2, and t + 3 were 9.25, 12.26, and 16.05 µg/m³, with R² above 0.90 up to the third day. For Dammam, RMSE values for the same horizons were 4.46, 7.24, and 11.34 µg/m³, and R² remained above 0.90 up to the fourth day. For Jeddah, the corresponding values were 3.97, 6.09, and 9.36 µg/m³, and R² remained above 0.90 up to the fourth day. The hybrid VMD–BiGRU model achieved higher accuracy for short horizons (t + 1 to t + 3). The study establishes a basis that aids short-term PM_2.5 prediction and improves air quality assessment across major urban centers in KSA.