A novel approach for detection and classification of re-entrant crack using modified CNNetwork

Purpose In the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Design/methodology/approach In the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method. Findings In the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results. Originality/value The originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.

A Case Study of Thaumasite Sulfate Attack in Tunnel Engineering

There existed ever-increasing structural diseases in the Dugongling tunnel after the completion of the construction. Systematic research was carried out based on the information of tunnel engineering geology, disease development, mineral composition of surrounding rock and concrete, and laboratory tests. Results showed that (1) the concrete structure from the Dugongling tunnel suffered from a typical thaumasite sulfate attack (TSA); (2) tunnel diseases developed under the influence of multiple geological factors such as groundwater, karst, gypsum bearing stratum, and geological tectonics. Groundwater aggravated the appearance of engineering defects of various geological factors and accelerated the softening and swelling of surrounding rock. Moreover, the additional load of supporting structure increased, and the concrete structure was prone to sulfate attack; (3) the development of tunnel diseases showed long-cycle and large-scale characteristics, and the types of diseases are heterogeneous. The tunnel diseases develop rapidly in the third year after the construction. The structure collapse disease, one of the most serious diseases of tunnel lining, occurred in the fifth year after construction; and (4) according to the conditions of TSA, technical treatment measures were proposed. These measures included blocking the flow of CO 3 2 − , improving the impermeability of concrete structures, and decreasing the diffusion of external ions and groundwater. This work provides a solution for the treatment of similar tunnel disease and mechanism analysis.

Novel Mangosteen-Leaves-Based Marker Ink: Color Lightness, Viscosity, Optimized Composition, and Microstructural Analysis

Dry mangosteen leaves are one of the raw materials used to produce marker ink. However, research using this free and abundant resource is rather limited. The less efficient one-factor-at-a-time (OFAT) approach was mostly used in past studies on plant-based marker ink. The use of statistical analysis and the regression coefficient model (mathematical model) was considered essential in predicting the best combination of factors in formulating mangosteen leaf-based marker ink. Ideally, ink should have maximum color lightness, minimum viscosity, and fast-drying speed. The objective of this study to study the effect of glycerol and carboxymethyl cellulose (CMC) on the color lightness and viscosity of mangosteen-leaves-based marker ink. The viscosity, color lightness, and drying properties of the ink were tested, the significant effect of glycerol and CMC (responses) on ink properties was identified and the prediction model on the optimum value of the responses was developed by using response surface methodology (RSM). The microstructure of mangosteen leaves was analyzed to study the surface morphology and cell structure during dye extraction. A low amount of glycerol used was found to increase the value of color lightness. A decrease in CMC amounts resulted in low viscosity of marker ink. The optimum formulation for the ink can be achieved when the weight percents of glycerol, benzalkonium chloride, ferrous sulphate, and CMC are set at 5, 5, 1, and 3, respectively. SEM micrographs showed the greatest amount of cell wall structure collapse on samples boiled with the lowest amount of glycerol.

Water-Stable Hydrazone-Linked Porous Organic Cages

Although porous organic cages (POCs), particularly imine-linked (C=N) ones, have advanced significantly over the last decades, the reversible nature of imine linkages makes them prone to hydrolysis and structure collapse,...

Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards

In a context of climate change, trends in extreme snow loads need to be determined to minimize the risk of structure collapse. We study trends in 50-year return levels of ground snow load (GSL) using non-stationary extreme value models. These trends are assessed at a mountain massif scale from GSL data, provided for the French Alps from 1959 to 2019 by a meteorological reanalysis and a snowpack model. Our results indicate a temporal decrease in 50-year return levels from 900 to 4200 m, significant in the northwest of the French Alps up to 2100 m. We detect the most important decrease at 900 m with an average of −30 % for return levels between 1960 and 2010. Despite these decreases, in 2019 return levels still exceed return levels designed for French building standards under a stationary assumption. At worst (i.e. at 1800 m), return levels exceed standards by 15 % on average, and half of the massifs exceed standards. We believe that these exceedances are due to questionable assumptions concerning the computation of standards. For example, these were devised with GSL, estimated from snow depth maxima and constant snow density set to 150 kg m−3, which underestimate typical GSL values for the snowpack.

Ophthalmic In Situ Gelling System Containing Lanosterol Nanoparticles Delays Collapse of Lens Structure in Shumiya Cataract Rats

We attempted to prepare ophthalmic in situ gel formulations containing lanosterol (Lan) nanoparticles (LA-NPs/ISG) and investigated the characteristics, delivery pathway into the lens, and anti-cataract effects of LA-NPs/ISG using SCR-N (rats with slight lens structure collapse) and SCR-C (rats with a combination of remarkable lens structure collapse and opacification). LA-NPs/ISG was prepared by bead milling of the dispersions containing 0.5% Lan powder, 5% 2-hydroxypropyl-β-cyclodextrin, 0.5% methylcellulose, 0.005% benzalkonium chloride, and 0.5% mannitol. The particle size distribution of Lan was 60–250 nm. The LA-NPs/ISG was gelled at 37 °C, and the LA-NPs/ISG was taken into the cornea by energy-dependent endocytosis and then released to the intraocular side. In addition, the Lan contents in the lenses of both SCR-N and SCR-C were increased by the repetitive instillation of LA-NPs/ISG (twice per day). The space and structure collapse in the lens of SCR-N with aging was attenuated by the instillation of LA-NPs/ISG. Moreover, the repetitive instillation of LA-NPs/ISG attenuated the changes in cataract-related factors (the enhancement of nitric oxide levels, calpain activity, lipid peroxidation levels, Ca2+ contents, and the decrease of Ca2+-ATPase activity) in the lenses of SCR-C, and the repetitive instillation of LA-NPs/ISG delayed the onset of opacification in the SCR-C. It is possible that the LA-NPs/ISG is useful in maintaining lens homeostasis.

Lithium perrhenate catalyst for carbon aerogel-based supercapacitor

Carbon aerogels are unique type of amorphous carbon-based materials with an extraordinary properties including light weight, well-developed and tailored pore structure, high specific surface area, low electrical resistivity and high purity. Therefore carbon aerogels can be used in numerous applications, including energy storage materials such as supercapacitors or lithium-ion batteries. Synthesis of hydrogel requires accurate concentration of monomers and catalysts, exact temperature and time. Low-density carbon aerogel can be produced using a complicated supercritical drying method, which allows to expel solvent without carbon porous structure collapse. Here we report a possibility to use novel type of resorcinol/formaldehyde gelation catalyst based on rhenium salt. The organic gel obtained using lithium perrhenate was traditionally carbonized and examined with respect to energy storage properties. It was found that interaction of lithium and rhenium atoms with monomers resulted in completely different structure than using typical sodium carbonate catalyst. Graphic abstract

Template-Free Fabrication of Refractive Index Tunable Polysiloxane Coating Using Homogeneous Embedding Strategy: Application in High-Power Laser System

A refractive index (RI) tunable polysiloxane coating was fabricated based on the cross-linked network structure embedded with mesoporous silica nanoparticles (MSNs), in which the MSNs were utilized to modulate the RI as well as to support the interior structure of the polysiloxane coating. The Si–O–Si inorganic backbone structure in combination with characteristics from the photopolymerization of active bonds produced the main cross-linked network structure, and controllable embedding of MSNs constructed the network-sphere structure. This approach eliminated the high-temperature post-treatment that was needed to remove the template, which ensures the safe application for temperature-sensitive laser crystal substrates and avoids coating structure collapse. In addition, degradation of the resulting coating can be minimized due to the similar chemical formation between MSN and polysiloxane coating. Hereby, a polysiloxane coating with expected spectral and laser damage-resistant properties can be obtained. This will facilitate the fabrication and application of a laser component with both high-transmission and high-flux capability for a high-power laser system.