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This paper presents a safety mechanism that is supposed to be used to enhance in the agricultural tractors. A tractor instability situation may be occurring when drawbar force becomes large enough to cause no load (weight) on its front axle. This endangers the tractor stability and the tractor will be overturned rearward. The proposed tractor safety mechanism is based on monitoring the location of tractor center of gravity and progressively shifting forward in a dead weight to counteract for the effect of tractors front lift-up. A laboratory tractor model has been developed in such a way that the lift of its front is sensed and accordingly a mechanism that shifts a movable dead weight ahead to the front a distance that automatically ensures its longitudinal stability. Such arrangement provides a solution that practically ensures longitudinal tractor stability in the situations when maximum drawbar-pull is suddenly developed.

The main objective of this research is to find a general empirical formula to predict vehicle frontal area applied to most types of vehicles. This was done on 21 vehicles; passenger cars, buses and trucks by calculating their frontal area by using image processing technique on cars photos extracted from catalogues. The software (Data Fit) is used to establish the required empirical formula. The results showed that the empirical formula is simple and accurate enough for finding out the vehicles frontal areas.

The bearing capacity of soils stands as one of the most important parameters that determine the vehicles’ off-road mobility. Soil bearing capacity can be determined either experimentally or by calculation using analytical and or empirical formulas. One of the most famous formulas is the Bekker's. Recently, Artificial Neural Networks (ANNs) technique became a powerful tool that can be used for predicting systems’ behavior and performance. The main objective of this paper is to predict the bearing capacity of the soil (plate-sinkage relationships) by using Artificial Neural Networks and to compare the actual results of soil bearing capacity (collected data from Ph.D. Thesis) with results obtained from neural network model and Bekker's formula. The comparison showed clear superiority and accuracy of neural network technique. Another objective is to check the generalization ability of the neural network model in predicting the plate-sinkage relationships by using the hypothetical plate.

The main aim of this paper is to optimize pneumatic tyre parameters related that improve tyre flotation and performance on sandy soils. Tyre flotation pressure is the pressure of inflation that makes tyre flotation maximum such that it deforms more than it sinks in the soil. A second aim is to predict the tyre flotation pressure on dry sandy soil by using Artificial Neural Networks (ANNs) Technique. A third aim is to predict a new tyre size that improves flotation on dry sand by using Artificial Neural Networks (ANNs) Technique. Experimental investigation has been carried out on three tubeless tyre of sizes (225/75R15, 235/70R15 and 255/60R15) inflated each with four inflation pressures (50, 100, 150 and 200 kPa) on three dry sand with three densities (Loose, Medium, and High). The investigation aimed at determines the tyre deflection-load, sinkage-load relationships were measured and the tyre flotation pressure.