Determination of relative damage of asphalt pavement from reduced tire contact area

Considering the traditional contact area which is a full circular contact area without any tread, in the current pavement design procedure, is an extreme overestimation of contact area and hence extreme underestimation of the real contact stress. Since the relationship between the contact stress and pavement damage is not linear but exponential, even a trivial difference between tire contact areas leads to significant difference in terms of induced pavement damage. This study was conducted to quantify the relative damage caused by realistic tire–pavement contact area with respect to the full contact area. Therefore, permanent deformation profiles of different contact areas at three tire load groups were obtained using an in-house Rotary Compactor and Wheel Tracker equipment and the relative damage analyses were done between tires with and without tread from various aspects. These aspects include operational life reduction ratio, rutting rate, linear and nonlinear relative damage concepts. It was concluded that on average real tires with tread cause 57% reduction in the operational life. In average, real tires with tread induce 1.23 times more mm per cycle. Based on linear relative damage analysis, in average, real tires with tread are 2.6 times more damaging. Furthermore, nonlinear relative damage analyses indicate that real tires with tread induce about three times more rutting compared to the worn-out control tread.     

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R ²) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R ² values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.     

Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.     

Several non-linear models in estimating air-overpressure resulting from mine blasting

This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R ²) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms of R ², RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.     

A new hybrid model of information entropy and unascertained measurement with different membership functions for evaluating destressability in burst-prone underground mines

The occurrence of unpredictable hazards are frequent with the increased depth of mining, especially the hazards caused by stress concentration. In order to mitigate the negative effectiveness results from mining-induce stress, various approaches have been employed in underground mines. Destress blasting, as an efficient method, has gained a lot of popularity in recent years. However, it is crucial to estimate the destressability of specific area before conducting destress blasting. In this study a combination model on the basis of both unascertained measurement (UM) and entropy coefficients was applied to observe the performance of destressability evaluation. Eight representative parameters, i.e., stiffness of the rock mass, brittleness of the rock mass, degree of fracturing, proximity to failure, destress blast orientation, width of the target zone, unit explosive energy, and confinement of the charges were chosen as initial input parameters, and their membership distributions were described by four types of membership methodologies, i.e., line, parabolic curve, exponential curve, and sine curve. Meanwhile, the weights of each index could be computed based on the single measurement matrix. Then, destressability of the samples was easily identified with Euclidean distance and comprehensive measurement vectors which were computed by single measurement vectors and weight coefficients. Finally, it was found that the assessment results are in accordance with those calculated by destressability index. It can be concluded that the proposed hybrid model is able to eliminate the disturbance of subjective factors and ensure the reliability of these outcomes. At the same time, it can provide a novel idea/process for the destressability evaluation.

     

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

Engineering with Computers - Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has...     

A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Optimization in dynamic environment is considered among prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel awakening–sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method.     

Prediction of cement-based mortars compressive strength using machine learning techniques

Neural Computing and Applications - The application of artificial neural networks in mapping the mechanical characteristics of the cement-based materials is underlined in previous investigations....     

TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm

Neural Computing and Applications - A reliable and accurate prediction of the tunnel boring machine (TBM) performance can assist in minimizing the relevant risks of high capital costs and in...     

Study on Failure Analyses and Material Characterizations of a Damaged Booster Pump

Pumps are a key and crucial part of many industrial units which usually are endangered by metallurgical, mechanical, and chemical damages. The most important mechanisms of failure in pumps are cavitation, erosion, and corrosion which directly are influenced by pump’s materials, type of fluent, and operation condition. The aim of this study was to investigate the role of material selection in the main failure mechanisms of a power plant booster pumps. To observe the kind and micro structure of pumps optical microscopy and image analyses software were used. Morphology of the pumps’ body is investigated by scanning electron microscopy. Electrochemical tests and water analyses are done for measurement of corrosion rate as well as amount of particles in feed water. Moreover, tensile testing was carried out to compare the mechanical properties of body alloy with standard alloy. The results revealed that cavitation and erosion were the most significant mechanisms. On the other hand, the data from analyses and observations clarified that the material which chosen for pumps alloy was improper which was accompanied with lack of fabrication technology.