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Earthmoving is one of the key activities in most heavy civil construction projects. The dump truck is one primary construction vehicle for earthmoving. Two popular approaches are currently used to estimate earthmoving volume by trucks, i.e., manually counting the number of loaded trucks and weighing loaded trucks on a scale station. Considering both methods are either error-prone, time-consuming, or costly, this study aims to estimate different earth volumes in dump trucks from a single image using the machine learning approach. By establishing a pre-trained deep learning neural network from 3663 images with sixteen different volumes of the earth using a scaled dump truck model, the proposed approach is tested to estimate the truckload in a quantitative manner in real-time. Another 1221 images are used for verification in six case combinations out of the sixteen different volumes. The preliminary results show that the classification accuracy by using the pre- trained network is 100% if the volume gap between adjacent classes is more than 5%, while 76.67% if the volume gap is 1%. The preliminary test results show a great potential that the proposed methods could be applied to the field and provide a fast and accurate estimate of truckload with minimal cost.

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This paper conducts an experimental comparison between two recently introduced meta-heuristic algorithms, which are the Differential Evolution (DE) and the Artificial Bee Colony (ABC) algorithm. Both these algorithms are very prominent and significant to represent the broader family of algorithms to which they belong, i.e., the Evolutionary and Swarm Intelligence algorithm families. Both DE and ABC have been successfully employed to numerous and diverse problems from the fields of mathematics, science and engineering. DE is an evolutionary algorithm that computes the vector differences between randomly picked candidate solution vectors and uses these differences to produce new, improved candidate solutions to advance its evolutionary search and optimization process. The ABC is a swarm intelligent algorithm that mimics the candidate solutions as a swarm of bees that forage across a search space for continuously better quality food sources (i.e., candidate solutions). The aim and focus of this paper is to present a side-by-side comparison of these two evolutionary and swarm intelligence algorithms on a common set of continuous benchmark problems to achieve a better understanding of their strengths, weaknesses and characteristics. The experimental results show that ABC is more explorative and can consistently avoid the local optima to locate the neighborhood of the global minimum, while DE is more exploitative to achieve an excellent level of fine tuning, but at the risk of premature convergence because of its lack of explorative characteristics.

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