Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system

Bond Strength Assessment of Normal Strength Concrete-Ultra-High-Performance Fiber Reinforced Concrete Using Repeated Drop-Weight Impact Test: Experimental and Machine Learning Technique

Ultra-high-performance concrete (UHPC) has been used in building joints due to its increased strength, crack resistance, and durability, serving as a repair material. However, efficient repair depends on whether the interfacial substrate can provide adequate bond strength under various loading scenarios. The objective of this study is to investigate the bonding behavior of composite U-shaped normal strength concrete-ultra-high-performance fiber reinforced concrete (NSC-UHPFRC) specimens using multiple drop-weight impact testing techniques. The composite interface was treated using grooving (Gst), natural fracture (Nst), and smoothing (Sst) techniques. Ensemble machine learning (ML) algorithms comprising XGBoost and CatBoost, support vector machine (SVM), and generalized linear machine (GLM) were employed to train and test the simulation dataset to forecast the impact failure strength (N2) composite U-shaped NSC-UHPFRC specimen. The results indicate that the reference NSC samples had the highest impact strength and surface treatment played a substantial role in ensuring the adequate bond strength of NSC-UHPFRC. NSC-UHPFRC-Nst can provide sufficient bond strength at the interface, resulting in a monolithic structure that can resist repeated drop-weight impact loads. NSC-UHPFRC-Sst and NSC-UHPFRC-Gst exhibit significant reductions in impact strength properties. The ensemble ML correctly predicts the failure strength of the NSC-UHPFRC composite. The XGBoost ensemble model gave coefficient of determination (R2) values of approximately 0.99 and 0.9643 at the training and testing stages. The highest predictions were obtained using the GLM model, with an R2 value of 0.9805 at the testing stage.

Exploring vehicle-centric strategies for sustainable urban mobility: A theoretical framework for saving energy and reducing noise in transportation

Adopting energy-saving and noise-reducing technologies in vehicle transportation has the potential to mitigate urban traffic pollution and promote sustainable urban mobility. However, a universal analytical framework for obtaining the combined energy savings and noise reduction patterns in vehicles is still lacking. This study addresses this gap by integrating a fundamental traffic noise model with a vehicle energy conservation equation. A theoretical framework was constructed that establishes the relationship between vehicle noise and energy consumption, with the theoretical origins of this framework explained. By summarizing a substantial body of classical literature, the typical model's properties are analyzed through the principle of optimality, and the noise interval for combined vehicle energy-saving and noise-reducing is determined. Subsequently, a rigorous vehicle experiment was conducted to validate the proposed framework's effectiveness, utilizing synchronized data on energy consumption and noise. The findings indicate that vehicles can achieve unconstrained combined energy-saving and noise-reducing in four driving states and conditional combined energy-saving and noise-reducing in five driving states. The Recall index demonstrates a verification rate exceeding 0.62 for the combined energy-saving and noise-reducing rules. This research provides valuable insights to support energy-saving and noise-reducing measures in urban traffic.

Predicting highly dynamic traffic noise using rotating mobile monitoring and machine learning method

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km², and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R² = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R² = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps.

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C₃A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R² values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration

Accuracy in the prediction of the particulate matter (PM_2.5 and PM₁₀) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM_2.5 and PM₁₀ concentration. The NF-E involves careful selection of relevant input parameters for base modelling and using an adaptive neuro fuzzy inference system (ANFIS) model as a nonlinear kernel for obtaining ensemble output. The four base models used include ANFIS, artificial neural network (ANN), support vector regression (SVR) and multilinear regression (MLR). The dominant input parameters for developing the base models were selected using two nonlinear approaches (mutual information and single-input single-output ANN-based sensitivity analysis) and a conventional Pearson correlation coefficient. The NF-E model was found to predict both PM_2.5 and PM₁₀ with higher generalization ability and least error. The NF-E model outperformed all the single base models and other linear ensemble techniques with a Nash-Sutcliffe efficiency (NSE) of 0.9594 and 0.9865, mean absolute error (MAE) of 1.63 μg/m³ and 1.66 μg/m³ and BIAS of 0.0760 and 0.0340 in the testing stage for PM_2.5 and PM₁₀, respectively. The NF-E could improve the efficiency of other models by 4-22% for PM_2.5 and 3-20% for PM₁₀ depending on the model.

Evaluation of urban inland waterway traffic noise using a modified Nord 2000 prediction model

This study developed a prediction model for estimating urban inland waterway traffic noise emission level. The model based on the Scandinavian Nord 2000 method, which was modified by adding two categories of traffic flow, comprising light and heavy vessels, as well as vessel average speed to the calculating equations. Meanwhile, the influences of the water surface and embankment were also considered in the established model. Model verification was conducted using the data surveyed at the 30 sampling points of Danjinlicaohe Channel in Jiangsu Province of China. A high correlation was found between the predicted and measured noise values L_Aeq (Pearson correlation coefficient = 0.949, p < 0.01). And the mean difference between the predicted and measured noise values was 0.16 ± 1.28 dBA. The results showed that the proposed model had higher accuracy than the unmodified Nord 2000 method and can be applied for predicting vessel noise exposure level on inland waterway of China.

Artificial intelligence based ensemble model for prediction of vehicular traffic noise

Vehicular traffic noise is the main source of noise pollution in major cities around the globe. A reliable and accurate method for the estimation of vehicular traffic noise is therefore essential for creating a healthy noise-free environment. In this study, 2 linear (simple average and weighted average) and 2-nonlinear (neural network and neuro-fuzzy) ensemble models were developed by combining the outputs of three Artificial Intelligence (AI) based non-linear models; Adaptive Neuro Fuzzy Inference System (ANFIS), Feed Forward Neural Network (FFNN), Support Vector Regression (SVR) and one Multilinear regression (MLR) model to enhance the performance of the single black box models in predicting vehicular traffic noise of Nicosia city, North Cyprus. In this way, first a nonlinear sensitivity analysis was applied to select the most relevant and dominant input parameters of the traffic data obtained from 12 observation points in the study area. The most dominant parameters in order of their importance were determined to be number of cars, number of van/pickups, number of trucks, average speed and number of buses. Classifying the number of vehicles into five categories before feeding the traffic data into the AI models was observed to improve performance of the single models up to 29% in the verification phase. Out of the four ensembles models developed, the nonlinear ANFIS ensemble was found to be the most robust by improving the performance of ANFIS, FFNN, SVR and MLR models in the verification stage by 11%, 19%, 21% and 31%, respectively.