# Influence of Crushed Rock Properties on the Productivity of a Hydraulic Excavator
## 1 Introduction  
Hydraulic excavators are common nowadays and are often irreplaceable equipment at the majority of mining and civil worksites [1]. In civil projects, hydraulic excavators are primarily used for earthmoving works. In mining, in open pits or quarries, they are used for many different tasks such as auxiliary equipment or main machinery for non-cohesive mineral raw material excavation. The excavator plays a significant role in the transition from the drill and blast excavation non-blasting methods [2]. Therefore, optimization of efficiency [3] and productivity of the excavators are necessary for on-time availability, cost reduction, and better performance. Many authors have analyzed the productivity of earthmoving machinery as a system consisting of an excavator and truck [4; 5; 6; 7; 8; 9], but calculation of the productivity of only the excavator was not conducted. In numerous studies, authors used miscellaneous statistical methods to model excavator productivity using different independent variables. In [10], multiple regression was used to model excavator productivity by employing the weight ofthe excavator, depth of digging, and swing angle as independent variables. For calculating excavator productivity, an artificial neural network was applied in [11; 12]. Irrespective of the calculation method or model of the excavator productivity used, analyses must begin by determining the technical excavator productivity. This depends on the volume of material in the bucket and the duration of the working cycle as is the case for most cyclical machinery. To obtain effective excavator productivity, technical productivity must be corrected by mainly modifying the estimation factors: operator competence, operator utilization, and excavator efficiency. Excavator efficiency is called \"random conditions\" [13] and \"natural variability\" [6] and depends on specific conditions at the work site. Operator competence positively correlates with a higher productivity of the excavator [14]. The impact of operator competence on operator utilization and excavator efficiency is analyzed in detail in [15], and a hybrid factor was developed which can be used to calculate effective excavator productivity as a function of operator competence alone. The cycle time of the excavator is composed of the time needed to fill the bucket, slewing time, and time for unloading the bucket [16]. The cycle time for filling and unloading the bucket is, principally, the consequence of material properties while the slewing time of the excavator depends on size of the machine, slew angle, and height/depth of the working face [17]. This can be monitored by using specific measurement systems [18], but productivity calculations also require material properties. The amount of material in the bucket is a function of the constructional bucket volume and bucket fill factor. According to an extensive literature review in [19], in the past three decades a significant amount of research has been carried out on the automation of excavating machinery. It has been stated that the capability to fill a bucket with material depends on the same material parameters as those for loosening the material from the ground. The extent of filling is higher for fine grained and homogenous material and lower for material with a high boulder content. Based on the studies conducted in [20; 21; 22], the main factors that govern the interconnection between the heap size distribution resulting from a production blast in surface mines and the productivity of the excavator are the cycle time and the bucket fill factor. In [23], the relationship between the characteristic heap fragment size and both the excavator cycle time and the bucket fill factor was investigated, and an estimation model was proposed as a tool for determining excavator productivity. The characteristic heap fragment size depends on the coefficient of uniformity of the heap size distribution and the mean fragment size of the heap [24].