A 1DCNN-Based Fine-Tuning of the Weighing System With GA for the Construction Equipment

A weighing system calculates the payload of an excavator bucket using sensor information. This technique has been employed to assess the work of the operators and to verify the termination point between the excavator and mobile construction equipment during collaborative operations. Certain commercial weighing devices, such as Trimble’s Loadrite, have measurement inaccuracies attributable to the restricted quantity of sensors. This research uses a deep learning model to mitigate measurement errors. Hyperparameters in deep learning are variables that must be predetermined prior to the learning process. To achieve optimal performance from a neural network, a user must engage in optimization, which is challenging due to several unknown variables. This work employs a genetic algorithm (GA) to optimize hyperparameters. The data was initially gathered during the loading and unloading activities. Subsequently, it was employed for a deep regression model. The data were converted to a structured format contingent upon the input shape of the network. A polynomial regression model was employed for the fully connected dense layer architecture. Strict criteria are necessary for evaluating deep learning models. A chosen model was refined through genetic algorithms and subsequently validated with loading and unloading tests using a different excavator.