• The possibility of using AE to determine vehicle loads on bridges is explored. • The single-attribute analysis is conducted for AE features. • An improved ensemble ANN is proposed to solve the imbalance problem. • The efficiency of the proposed algorithms is discussed. Bridges are significant hubs in the U.S. national economy, facilitating the movement of goods and vehicles. The condition of bridges in the state of South Carolina is currently under scrutiny, especially in rural areas where most of the bridges were designed using outdated standards from the 1950 s. The weight of vehicles in recent years has increased significantly compared to the past. This has created an overloading problem. In addition, bridge performance decreases during their service life due to vehicle loads, material deterioration, and environmental erosion. Therefore, it is necessary to inspect and conduct load ratings on bridges to determine whether the bridges need to be posted. Due to recent advances in sensing technology and data analysis methods, nondestructive methods such as acoustic emission (AE) have been widely utilized in monitoring damage to the bridges. The objective of this paper is to explore the possibility of using AE sensors concurrently to determine vehicle loads on the bridges while monitoring bridge damage. A load determination method leveraging an improved ensemble artificial neural network (ANN) is proposed to analyze the AE data and estimate the load of the vehicle. The significance of this vehicle load determination method is that it has the potential to be paired with an AE damage monitoring system rather than using other instrumentation such as a weigh-in-motion (WIM) system. The proposed method has been tested on an experimental bridge component. The results suggest that the proposed model has an accuracy above 70 % in estimating the vehicle loads on the precast reinforced concrete (RC) flat slabs. [ABSTRACT FROM AUTHOR]