Agricultural Structures and Mechanization

In our globalized world, the need to produce quality and safe food has increased exponentially in recent decades to meet the growing demands of the world population. This expectation is being met by acting at multiple levels, but mainly through the introduction of new technologies in the agricultural and agri-food sectors. In this context, agricultural, livestock, agro-industrial buildings, and agrarian infrastructure are being built on the basis of a sophisticated design that integrates environmental, landscape, and occupational safety, new construction materials, new facilities, and mechanization with state-of-the-art automatic systems, using calculation models and computer programs. It is necessary to promote research and dissemination of results in the field of mechanization and agricultural structures, specifically with regard to farm building and rural landscape, land and water use and environment, power and machinery, information systems and precision farming, processing and post-harvest technology and logistics, energy and non-food production technology, systems engineering and management, and fruit and vegetable cultivation systems. This Special Issue focuses on the role that mechanization and agricultural structures play in the production of high-quality food and continuously over time. For this reason, it publishes highly interdisciplinary quality studies from disparate research fields including agriculture, engineering design, calculation and modeling, landscaping, environmentalism, and even ergonomics and occupational risk prevention

Tractive performance of 4x4 tyre treads on pure sand.

This thesis examined the difficulties of generating traction from 4x4 (light truck) tyres in pure sand conditions. Investigations conducted in the Cranfield University Soil Dynamics Laboratory measured the tractive performance of a range of production and prototype 4x4 tyre tread patterns to quantify the effect of tread features upon tractive performance. The investigation also quantified the amount of sand displacement instantaneously occurring beneath the tyre, by a novel application of radio frequency identification (RFID) technology, which determined sand displacements to an accuracy of ±5.5 mm. A limited number of normal contact stress measurements were recorded using a TekScan normal pressure mapping system. This technology was employed in a new manner that allowed pressure distributions to be dynamically recorded on a deformable soil surface. Models were developed or adapted to predict rolling resistance, gross thrust of a tyre and the gross thrust effect due to its tread. Net thrust was predicted from refined versions of equations developed by Bekker to predict gross thrust and rolling resistance. These were modified to account for dynamic tractive conditions. A new tread model proposed by the author produced a numerical representation of the gross thrust capability of a tread based on factors hypothesised to influence traction on loose sand. This allowed the development of a relationship between the features of the tread and its measured gross thrust improvement (relative to a plain tread tyre), from which a total relationship was developed. The tread features were also, in combination with the wheel slip, related to the sand displacements and net thrusts simultaneously achieved. The sand displacement results indicated that the majority of the variation in displacement between the different treads occurred in the longitudinal (rearward) direction. This effect was influenced by the wheel slip, as increased slip caused greater displacements, so the differences between the treads were greater at higher slips. The treads that generated the highest relative displacements also derived the higher gross thrusts (up to +5% extra gross thrust compared to a plain tread), although at the higher slips this also caused increased sinkage. As sinkage increased, the rolling resistance increased at a fester rate then the gross thrust, and thus the net thrust reduced. To prevent this effect the wheel slip should be limited to a maximum of 20% at low forward speeds (approximately 5 km/h). Current market forces dictate that the biggest benefit that tyre manufacturers could offer in desert market regions would be to optimise road-biased tyres to suit loose sand conditions. The modelling developed indicated that this could be achieved by maximising the number of lateral grooves (and thus lateral edges) featured on a tread, however care would have to be exercised so as not to compromise the necessaiy on-road capability. The models could also be used to quantifiably determine from a choice of possible tyre treads, the tread that would offer most traction on pure loose sand

Discrete element and artificial intelligence modeling of rock properties and formation failure in advance of shovel excavation

Rock tests are performed before the start of every mining or civil engineering project as part of a detailed feasibility study. The feasibility study is costly and it comprises drilling, sample collection, sample handling and laboratory testing. Numerical modeling techniques, such as Particle Flow Code (PFC), can be used to provide reliable estimates of rock strength values. The numerical models for unconfined compressive strength (UCS), direct tension, and Brazilian tests were developed in PFC, and validated using data from literature. A particle size range of 3-5 mm with Dmax/Dmin = 1.67 gave the best results. The numerical errors were in the range of 6-22% for UCS, 21-80% for direct tension, and 5- 10% for Brazilian tests. About 1,800 confined compression tests were also performed in PFC to obtain formation material properties. However, the PFC algorithm takes a very long computational time to complete the process, and thus, there is a need for more efficient and faster methods. In this research, the author uses artificial intelligence methods including, Artificial Neural Network, Mamdani Fuzzy Logic, and Hybrid neural Fuzzy Inference System (HyFIS) to solve this problem. These methods, along with the Multiple Linear Regression method, were used for the predictive analysis. Based on R2 and RMSE statistics for the testing phase, HyFIS is the best predictive model. This study is the first attempt to develop self-learning artificial intelligent models for predicting formation material properties. In addition, this research study investigates the shovel excavation process using the discrete element technique in PFC to examine the shovel digging phase. The shovel excavation simulator provides a tool for optimizing strategies for maximizing its performance that provides a major breakthrough in the shovel excavation frontier --Abstract, page iii