The blasting operations in open-pit mines are one of the most sensitive and complex mining methods which are defined under special circumstances and specific purposes. The suitable and optimal pattern selection where could be capable to covering the various environmental factors and affected on economic, technical, engineering and safety is the main goal of the operation. The genetic algorithm (GA) is the recommended method to utilizing the optimized pattern for the blasting pattern selection under the many effective parameters which is the variety of noteworthy detected on selected pattern from different mines. In this work the GA used to propose optimal blasting operation pattern by consideration of good coverage with field and geo-parametric in open-pit mine. For this purpose, the optimal design simulation of blasting pattern for open pit mines with one and two work-faces (for two standard 100×100 and 70×40m sides) by the Python programming language was conducted. Based on the results, this algorithm is quite successful in simulating the blasting pattern which it providing field decorations, the collapse (damage) rate and maximized exploitation (excavation). تفاصيل المقالة

This study attempted to use the image processing techniques for estimating the discontinuity network detection in dimension stones from open-pit mining work front. The mentioned processing techniques are utilized to determine the discontinuities distribution of the rock mass as geometrical properties charging instability condition in excavation process in quarry. To evaluate the discontinuity network detection from quarry work front used image processing-based algorithm were run in Python programming languages. The algorithm, first of all, identifies the discontinuities and generates the network of discontinuities emplacements to model the rock body in quarry work front. To this end, the triple processing steps included pre-processing, main processing and post-processing are conducted to model. According to the results of computer-based simulation of discontinuity network, the used algorithm is capable to detect the discontinuities emplacements in rock mass and recognize the main rock block spatial distribution in quarry work front. تفاصيل المقالة

The purpose of this study is application of fuzzy-based decision-making system (FMCDM) method and a device for monitoring and controlling water consumption in a smart house used for water supply management (WSM) based on intelligent water management system (IWMS). This paper presents an algorithm applied for water demand consumption (WDC) optimization in regular household and its capability to evaluate the WDC range and decision and controlled WSM in critical circumstances. For this purpose, the heuristic decision-making method are used to monitor and control the WCM as effective IWMS methods based on artificial intelligence technology (AIT) and smart-learning multi-sensor. To program the device software, the MATLAB fuzzy logic programming software has been used. In order to assess the device’s capability to respond to real conditions, a device is run to study sample-data for 24 hours a day for a month. In this study, four locations (kitchen, restroom, bathroom and garden balcony) are considered as the main WDC locations, then FMCDM algorithm are applied on these targets by main device. The results indicate that the first place (kitchen) has the greatest impact on WDC. However, the fourth place (garden balcony) has the lowest impact on WDC as well water consumption volume (WCV). Moreover, for analysis justification, the expert system (ES) is conducted as a comparison that shows the appropriate agreement with the FMCDM results. تفاصيل المقالة