• Journal of Korean Tunnelling and Underground Space Association
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• v.3 no.1
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• pp.3-9
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• 2001

The investigation of the fragmentation of blasted rocks is particularly important because it is a measure of the blast efficiency. The degree of fragmentation has a major effect on the efficiency of the loading and crushing operations. Getting such an information on the large pile of blasted rock is not an easy operation. This paper presents the results of case study to evaluate the performance of two types of tunnel blasting: V-cut and burn cut. The digital images of muckpiles were analyzed to produce size distribution and it was compared with those of predictive equations.

• Explosives and Blasting
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• v.22 no.3
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• pp.43-55
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• 2004

Use term of tower style construction exceeds recently. Accordingly, according to construction safety diagnosis result, achieve removal or Improvement construction. But when work removal, must shorten shut down time. Therefore, removal method of construction to use blasting demolition of construction is very profitable. Influence construction and equipment by blasting vibration and occurrence vibration caused by felling impact. Is using disadvantageous machine removal method of construction by and economic performance by effect of such vibartion. Therefore, this research studied techniques to forecast vibartion level happened at blasting demolition and vibration reduction techniques by use a scaled model test.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.259-269
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• 2017

Gust load is a very important load factor in designing various structures of an aircraft and judging its stability. This is because the blast effect on the aircraft in operation increases the risk of damage to the structure of the aircraft and causes a negative impact such as shortening the fatigue life by generating vibration. Particularly in the case of wing, a change in angle of attack is caused by gust load, and an additional lift acts on the wing, thereby being exposed to various excitational environments. Severe structural damage to the aircraft may occur if the natural frequencies of the aircraft wing are close to or coincident with the frequencies of the gust load applied to the wing. Recent trends of research include flight dynamics analysis considering discontinuous gusts or structural optimization of the blades under gust load. A number of studies have been conducted to interpret gust load response in consideration of irregularities in gusts. In this paper, we tried to imagine the situation of the aircraft subjected to the gust load as realistic as possible, and proposed an algorithm to track back the critical gust profile according to given aircraft characteristics from the viewpoint of preliminary engineering prediction.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.573-576
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• 2006

To predict service life of concrete structures exposed to chloride attack, surface chloride concentration, diffusion coefficient of chloride ion, and chloride corrosion threshold value in concrete, are used as important factors. of these, as the diffusion coefficient of chloride ion for concrete is strongly influenced by concrete quality and environmental conditions of structures and may significantly change the service life of structures, it is considered as the most important factor for service life prediction. The qualitative factors affecting the penetration and diffusion of chloride ion into concrete are water-binder(W/B) ratio, age, cement type and constituents, chloride ion concentration of given environment, wet and dry conditions, etc. In this paper the influence of cement type on the diffusion characteristics of chloride ion in concrete was investigated through the chloride ion diffusion test. For this purpose, the diffusion characteristics in concrete with cement type such as ordinary portland cement(OPC), binary blended cement(BBC), and ternary blended cement(TBC) were estimated for the concrete with W/B ratios of 32% and 38%, respectively. It was observed from the test that the difussion characteristics of BBC containing OPC and ground granulated blast-furnace slag was found to be most excellent of the cement type used in this study.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Microbiology and Biotechnology Letters
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• v.47 no.2
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• pp.278-288
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• 2019

In the present investigation, we attempted to design a protocol to develop a hybrid protein with better bioremediation capacity. Using in silico approaches, a Hybrid Open Reading Frame (Hybrid ORF) is developed targeting the genes of microorganisms known for degradation of organophosphates. Out of 21 genes identified through BLAST search, 8 structurally similar genes (opdA, opd, opaA, pte RO, pdeA, parC, mpd and phnE) involved in biodegradation were screened. Gene conservational analysis categorizes these organophosphates degrading 8 genes into 4 super families i.e., Metallo-dependent hydrolases, Lactamase B, MPP and TM_PBP2 superfamily. Hybrid protein structure was modeled using multi-template homology modeling (3S07_A; 99%, 1P9E_A; 98%, 2ZO9_B; 33%, 2DXL_A; 33%) by $Schr{\ddot{o}}dinger$ software suit version 10.4.018. Structural verification of protein models was done using Ramachandran plot, it was showing 96.0% residue in the favored region, which was verified using RAMPAGE. The phosphotriesterase protein was showing the highest structural similarity with hybrid protein having raw score 984. The 5 binding sites of hybrid protein were identified through binding site prediction. The docking study shows that hybrid protein potentially interacts with 10 different organophosphates. The study results indicate that the hybrid protein designed has the capability of degrading a wide range of organophosphate compounds.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.1
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• pp.44-50
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• 2024

In order to reasonably predict damage extents of naval ships under in-compartment explosion (INCEX) loads, two conditions should be fulfilled in terms of accurate INCEX load generation and fracture estimation. This paper seeks to predict damage extents of various naval ships by applying the CONWEP model to generate INCEX loads, combined with the Hosford-Coulomb (HC) and localized necking (LN) fracture model. This study selected a naval ship with a 2,000-ton displacement, using associated specifications collected from references. The CONWEP model that is embedded in a commercial finite element analysis software ABAQUS/Explicit was used for INCEX load generation. The combined HC-LN model was used to simulate fracture initiation and propagation. The permanent failures with some structural fractures occurred where at the locations closest to the explosion source points in case of the near field explosions, while, some significant fractures were observed in way of the interfaces between bulkheads and curtain plates under far field explosion. A large thickness difference would lead to those interface failures. It is expected that the findings of this study enhances the vulnerability design of naval ships, enabling more accurate predictions of damage extents under INCEX loads.

• Steel and Composite Structures
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• v.50 no.4
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• pp.443-458
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• 2024

The construction industry, one of the biggest producers of greenhouse emissions, is under a lot of pressure as a result of growing worries about how climate change may affect local communities. Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues connected to the manufacture of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete, which might be used in lieu of traditional concrete to reduce CO₂ emissions in the building industry. In the present work, the compressive strength (f_c) of GPC is calculated using random forests regression (RFR) methodology where natural zeolite (NZ) and silica fume (SF) replace ground granulated blast-furnace slag (GGBFS). From the literature, a thorough set of experimental experiments on GPC samples were compiled, totaling 254 data rows. The considered RFR integrated with artificial hummingbird optimization (AHA), black widow optimization algorithm (BWOA), and chimp optimization algorithm (ChOA), abbreviated as ARFR, BRFR, and CRFR. The outcomes obtained for RFR models demonstrated satisfactory performance across all evaluation metrics in the prediction procedure. For R² metric, the CRFR model gained 0.9988 and 0.9981 in the train and test data set higher than those for BRFR (0.9982 and 0.9969), followed by ARFR (0.9971 and 0.9956). Some other error and distribution metrics depicted a roughly 50% improvement for CRFR respect to ARFR.

• Journal of Microbiology and Biotechnology
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• v.20 no.2
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• pp.301-310
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• 2010

Bacillus subtilis strains produce a broad spectrum of bioactive peptides. The lipopeptide surfactin belongs to one well-known class, which includes amphiphilic membrane-active biosurfactants and peptide antibiotics. Both the srfA promoter and the ComP-ComA signal transduction system are an important part of the factor that results in the production of surfactin. Bs-M49, obtained by means of low-energy ion implantation in wild-type Bs-916, produced significantly lower levels of surfactin, and had no obvious effects against R. solani. Occasionally, we found strain Bs-M49 decreased spore formation and the development of competence. Blast comparison of the sequences from Bs-916 and M49 indicate that there is no difference in the srfA operon promoter PsrfA, but there are differences in the coding sequence of the comA gene. These differences result in three missense mutations within the M49 ComA protein. RT-PCR analyses results showed that the expression levels of selected genes involved in competence and sporulation in both the wild-type Bs-916 and mutant M49 strains were significantly different. When we integrated the comA ORF into the chromosome of M49 at the amyE locus, M49 restored hemolytic activity and antifungal activity. Then, HPLC analyses results also showed the comA-complemented strain had a similar ability to produce surf actin with wild-type strain Bs-916. These data suggested that the mutation of three key amino acids in ComA greatly affected the biological activity of Bacillus subtilis. ComA protein 3D structure prediction and motif search prediction indicated that ComA has two obvious motifs common to response regulator proteins, which are the N-terminal response regulator receiver motif and the C-terminal helix-turn-helix motif. The three residues in the ComA N-terminal portion may be involved in phosphorylation activation mechanism. These structural prediction results implicate that three mutated residues in the ComA protein may play an important role in the formation of a salt-bridge to the phosphoryl group keeping active conformation to subsequent regulation of the expression of downstream genes.