Muhd Zaimi Abd Majid

A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration

Drilling and blasting is an inseparable part of the rock fragmentation process in open-pit mines. Prediction of blast-produced ground vibration is considered as an important issue in blasting works. The aim of this study is to propose a hybrid model for predicting blast-produced ground vibration in the Miduk copper mine, Iran, using combination of the artificial neural network (ANN) combined with artificial bee colony (ABC) (codename ABC-ANN). Here, ABC was used as an optimization algorithm to adjust weights and biases of the ANN. The predicted values of ground vibration by ANN and ABC-ANN models were also compared with several empirical models. In this regard, 89 blasting events were monitored and values of two influential factors on ground vibration, i.e., maximum charge weight used per delay (MC) and distance between monitoring station and blasting-point (DI) together with their peak particle velocity values (as an index of ground vibration) were carefully measured. The results of the predictive models have been compared with the data at hand using mean absolute percentage error, root mean squared error and coefficient of correlation (R2) criteria. Eventually, it was indicated that the constructed ABC-ANN model outperforms the other models in terms of the prediction accuracy and the generalization capability.

An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

Airblast prediction through a hybrid genetic algorithm-ANN model

Air overpressure is one of the most undesirable destructive effects induced by blasting operation. Hence, a precise prediction of AOp has vital importance to minimize or reduce the environmental effects. This paper presents the development of two artificial intelligence techniques, namely artificial neural network (ANN) and ANN based on genetic algorithm (GA) for prediction of AOp. For this purpose, a database was compiled from 97 blasting events in a granite quarry in Penang, Malaysia. The values of maximum charge per delay and the distance from the blast-face were set as model inputs to predict AOp. To verify the quality and reliability of the ANN and GA-ANN models, several statistical functions, i.e., root means square error (RMSE), coefficient of determination (R2) and variance account for (VAF) were calculated. Based on the obtained results, the GA-ANN model is found to be better than ANN model in estimating AOp induced by blasting. Considering only testing datasets, values of 0.965, 0.857, 0.77 and 0.82 for R2, 96.380, 84.257, 70.07 and 78.06 for VAF, and 0.049, 0.117, 8.62 and 6.54 for RMSE were obtained for GA-ANN, ANN, USBM and MLR models, respectively, which prove superiority of the GA-ANN in AOp prediction. It can be concluded that GA-ANN model can perform better compared to other implemented models in predicting AOp.

Improvement of problematic soils with biopolymer-an environmentally friendly soil stabilizer

AbstractProblematic soils with high compressibility and low shear strength are often treated with traditional chemical stabilizing additives such as cement and lime to improve their engineering pro...

Lipid production by microalgae Chlorella pyrenoidosa cultivated in palm oil mill effluent (POME) using hybrid photo bioreactor (HPBR)

Palm oil mill effluent (POME) as high organic wastewater is a promising substrate in the scenario of algae bloom, by enhancing its lipid production to be further used in biofuel manufacturing. In this research, effect of POME as high nutritional substrate, different cultivation scales such as flask or hybrid photo bioreactor (HPBR), carbon-to-total nitrogen (C:TN) ratio, various light and dark cycles, and diverse organic loading rates (OLR) on the lipid productivity of microalgae Chlorella pyrenoidosa was assessed. Results demonstrated high microalgae growth rate (1.80 d �1 ) at 250 mg COD/L of substrate, while moderate increase (1.37 d �1 ) and growth inhibition (0.80 d �1 ) were recorded at 500 mg COD/L and 1,000 mg COD/L of substrate concentration, respectively. Furthermore, a result proved that low-volume cultivation of microalgae in a flask with lipid productivity at 1.78 mg/L d significantly restricted microalgae production compared with larger scale such as HPBR with lipid productivity at 230 mg/L d. Moreover, highest lipid production at 44.5, 114.9, and 100.5 mg/L d, C:TN ratio at 100:6 and OLR at 36 kg COD/m 3 d, respectively, were documented for continuous illuminaion (24 h). The combination of above conditions can be optimal setting to reach the highest lipid productivity by microalgae C. pyrenoidosa. In addition, the results of this study can be further considered in microalgae lipid production using other wastewaters in order to enhance the lipid production as well as wastewater treating functions.

Critical Criteria on Client and Customer Satisfaction for the Issue of Performance Measurement

Successful performance measurement criteria cannot be limited to meeting just the three traditional criteria. Satisfaction is a subjective and critical measurement for the stakeholder performance, but it has rarely been used as a criterion for the performance measurement of project stakeholders. The purpose of this paper is to establish the client and customer satisfaction criteria as the two key stakeholders in construction project for the issue of performance measurement based on the reviewed data. The methodology of this study is based on comprehensive literature review of performance measurements for client and customer whereby the data were analyzed, using the metrics which are the aggregate number of each customer and client-satisfaction criteria occurring in previous study. From the metric analysis, the common factors for customer and client satisfaction were ranked. From the analysis, it can be concluded that expectation and perception are the two common critical satisfaction criteria for client and customer that must be considered where the satisfaction is required. DOI: 10.1061/(ASCE)ME.1943-5479.0000183.

An Automatic Project Progress Monitoring Model by Integrating Auto CAD and Digital Photos

An assembly for housing microfilm is provided. The assembly includes a first cartridge, a second cartridge, and a connector assembly for detachably connecting the first cartridge to the second cartridge along an offset axis of the assembly. The first and second cartridges have a base plate and a cover plate secured together by a second connector assembly. The first connector assembly pivotally connects the two base plates together at a pivot point to allow each of the cartridges to rotate about the offset axis. The offset axis extends through the pivot point and is perpendicular to the plane formed by the junction of the first cartridge and the second cartridge.

A Monte Carlo technique in safety assessment of slope under seismic condition

In geotechnical engineering, stabilization of slopes is one of the significant issues that needs to be considered especially in seismic situation. Evaluation and precise prediction of factor of safety (FOS) of slopes can be useful for designing/analyzing very important structures such as dams and highways. Hence, in the present study, an attempt has been done to evaluate/predict FOS of many homogenous slopes in different conditions using Monte Carlo (MC) simulation technique. For achieving this aim, the most important parameters on the FOS were investigated, and finally, slope height (H), slope angle (α), cohesion (C), angle of internal friction (

Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems

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