Inventory of FRP strengthening methods in masonry structures

Masonry structures are prone to extensive damage followed by failure and collapse when subjected to loads resulting from wind, earthquake and other natural or man-made events. Recent earthquakes and terrorist acts have clearly demonstrated that the development of effective and affordable strategies for the strengthening of masonry is urgently needed. As a response to these challenges, fiber-reinforced polymer (FRP) composites may offer technically and economically viable solutions. In the context of work undertaken worldwide, this paper presents an overview of research studies and field applications of masonry strengthening with FRP composites as conducted in the last few decades. In particular, the thesis covers material forms and installation techniques, namely: externally bonded laminates, near surface mounted bars, and post-tensioning; experimental test programs dealing with the out-of-plane and in-plane behavior of walls, columns and arches with discussion of failure modes, field validation, and durability analysis and applications including historical structures. Without providing full details, an effort has been made to address issues related to design so that practicing engineers can immediately appreciate the potential of this technology and understand the key parameters affecting performance and the areas that need further experimentations

Inventory of FRP strengthening methods in masonry structures

Masonry structures are prone to extensive damage followed by failure and collapse when subjected to loads resulting from wind, earthquake and other natural or man-made events. Recent earthquakes and terrorist acts have clearly demonstrated that the development of effective and affordable strategies for the strengthening of masonry is urgently needed. As a response to these challenges, fiber-reinforced polymer (FRP) composites may offer technically and economically viable solutions. In the context of work undertaken worldwide, this paper presents an overview of research studies and field applications of masonry strengthening with FRP composites as conducted in the last few decades. In particular, the thesis covers material forms and installation techniques, namely: externally bonded laminates, near surface mounted bars, and post-tensioning; experimental test programs dealing with the out-of-plane and in-plane behavior of walls, columns and arches with discussion of failure modes, field validation, and durability analysis and applications including historical structures. Without providing full details, an effort has been made to address issues related to design so that practicing engineers can immediately appreciate the potential of this technology and understand the key parameters affecting performance and the areas that need further experimentations

Structural Responses Data Measured in an Instrumented Flexible Pavement

 This study presents and analyses the stress-strain responses data measured under real traffic conditions measured between Oct. 2012 to Oct. 2013 on an instrumented flexible pavement section on Interstate 40 (I-40) in the state of New Mexico, USA. Some weather variations data such as moisture and temperature variations at different depths of the pavement over the entire year are also discussed. The moduli of different layers determined using laboratory and field tests are also presented. It is expected that results of this study will be greatly useful to understand the behaviour of flexible pavement. The data presented in this study can be used to validate any constitutive or numerical model developed by readers

Removal of Arsenic from Contaminated Water by Granular Activated Carbon Embedded with Nano scale Zero-valent Iron.

This study investigated the removal of arsenic from groundwater by granular activated carbon (GAC) supported nano scale zero-valent iron (nZVI). GAC supported nZVI (nZVI/GAC) composite was synthesized by hydrolyzing a Fe(III) salt on GAC, reduced by NaBH4 and dried under vacuum. Synthesized nZVI/GAC was characterized using scanning electron microscopy (SEM) along with EDS, BET surface area analysis, X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. The experimental results were produced through the batch and Rapid Small Scale Column Test (RSSCT). The adsorption depends on pH, initial concentration, and reaction time. Arsenite adsorption capacity varies from 800 to 1400 μg/g over the pH 2-11. Arsenate adsorption was higher (3000-3700 μg/g) over the acidic pH range 2-6.5. Among competitive ions, phosphate and silicate affected the most while sulfate, nitrate, chloride, fluoride, manganese, magnesium and calcium had insignificant impact. The experimental data were evaluated with Langmuir and Freundlich isotherms. The adsorption capacity for arsenate, calculated from Langmuir and Freundlich isotherms, were 5000 and 6000 µg/g, respectively at pH 4.5. The reaction kinetics followed the pseudo-second order model. The initial sorption rate (h), determined from pseudo-second order kinetic model, was 666 µg/g.min. The dynamic behaviour of the RSSCT was predicted by the HSDM model using the software FAST 2.0. From the RSSCT results, it was found that the number of bed volumes treated depends on the empty bed contact time (EBCT) as well as the initial arsenate concentration. The regeneration of spent nZVI/GAC using 0.1M NaOH was effective as it desorbed 87% of adsorbed arsenic. The solid waste can be safely disposed of in a sanitary landfill without any treatment as the concentration of leached arsenate determined by TCLP was much lower than the regulatory limit. The arsenic removal mechanism was due to the combination of electrostatic and the complex formation, either monodentate or bidentate, between As(V) and nZVI corrosion products. The results indicated that nZVI/GAC is a promising adsorbent for arsenic removal

Biosorption of arsenic by anaerobic biomass

Arsenic is known around the globe in recent history due to the consequence of mass poisoning through the exposure of drinking water. Due its carcinogenic and many other adverse health effects, the regulatory authorities like the world health organization (WHO), United States environmental protection agency (USEPA) have reduced the maximum contamination level (MCL) of total arsenic in drinking water from 50 og/L to 10 og/L. Biosorption, a process of passive sequestration of contaminant materials by some dead and inactive biomass, has been preferred in this study to remove arsenic from contaminated water due to its eco-friendly nature and cost-effectiveness in comparison to the conventional technologies. An anaerobic digestion sludge obtained from a wastewater treatment plant was introduced to remove inorganic arsenic from contaminated water. The biomass was prepared in a granular form by drying, grinding and sieving. This granular biomass was investigated in equilibrium batch experiments and used in a continuous flow fixed-bed column operation. The biomass was also treated with KH 2 PO 4 and KCl to improve the sorption capacity but treatment did not contribute to the improvement as anticipated. Removal of arsenate [As (V)] was found pH dependent with the maximum removal at a pH range of 5 to 6, whereas arsenite [As (III)] was almost insensitive to pH over a range of 3 to 10. Initial arsenic concentration and contact time, in addition to pH, affected the biosorption capacity. The biosorption capacity of arsenate [As (V)] was 152 og/g at a pH of 5 and that of arsenite [(As (III)] was 60 og/g at a pH of 8 at an initial arsenic concentration of 2000 og/L for both cases. Adsorption data fitted with Langmuir isotherm model. Kinetic data followed pseudo-second-order model. A 40-minute contact time was sufficient to complete almost 95% of the total biosorption. In column operation, at a pH value of 5, 90 and 220 bed volumes of contaminated water with the respective arsenate concentration of 500 og/L and 200 og/L were treated by bringing the concentrations down to the regulatory limit of 10 og/L. Desorption of almost 40% arsenate was achieved using 0.5M NaCl solution. Protein/amino acid-arsenic interaction was proposed as the dominant mechanism in the biosorption proces

Removal of Arsenic from Contaminated Water by Granular Activated Carbon Embedded with Nano scale Zero-valent Iron

This study investigated the removal of arsenic from groundwater by granular activated carbon (GAC) supported nano scale zero-valent iron (nZVI). GAC supported nZVI (nZVI/GAC) composite was synthesized by hydrolyzing a Fe(III) salt on GAC, reduced by NaBH4 and dried under vacuum. Synthesized nZVI/GAC was characterized using scanning electron microscopy (SEM) along with EDS, BET surface area analysis, X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. The experimental results were produced through the batch and Rapid Small Scale Column Test (RSSCT). The adsorption depends on pH, initial concentration, and reaction time. Arsenite adsorption capacity varies from 800 to 1400 μg/g over the pH 2-11. Arsenate adsorption was higher (3000-3700 μg/g) over the acidic pH range 2-6.5. Among competitive ions, phosphate and silicate affected the most while sulfate, nitrate, chloride, fluoride, manganese, magnesium and calcium had insignificant impact. The experimental data were evaluated with Langmuir and Freundlich isotherms. The adsorption capacity for arsenate, calculated from Langmuir and Freundlich isotherms, were 5000 and 6000 µg/g, respectively at pH 4.5. The reaction kinetics followed the pseudo-second order model. The initial sorption rate (h), determined from pseudo-second order kinetic model, was 666 µg/g.min. The dynamic behaviour of the RSSCT was predicted by the HSDM model using the software FAST 2.0. From the RSSCT results, it was found that the number of bed volumes treated depends on the empty bed contact time (EBCT) as well as the initial arsenate concentration. The regeneration of spent nZVI/GAC using 0.1M NaOH was effective as it desorbed 87% of adsorbed arsenic. The solid waste can be safely disposed of in a sanitary landfill without any treatment as the concentration of leached arsenate determined by TCLP was much lower than the regulatory limit. The arsenic removal mechanism was due to the combination of electrostatic and the complex formation, either monodentate or bidentate, between As(V) and nZVI corrosion products. The results indicated that nZVI/GAC is a promising adsorbent for arsenic removal

Removal of Arsenic from Contaminated Water by Granular Activated Carbon Embedded with Nano scale Zero-valent Iron.

This study investigated the removal of arsenic from groundwater by granular activated carbon (GAC) supported nano scale zero-valent iron (nZVI). GAC supported nZVI (nZVI/GAC) composite was synthesized by hydrolyzing a Fe(III) salt on GAC, reduced by NaBH4 and dried under vacuum. Synthesized nZVI/GAC was characterized using scanning electron microscopy (SEM) along with EDS, BET surface area analysis, X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. The experimental results were produced through the batch and Rapid Small Scale Column Test (RSSCT). The adsorption depends on pH, initial concentration, and reaction time. Arsenite adsorption capacity varies from 800 to 1400 μg/g over the pH 2-11. Arsenate adsorption was higher (3000-3700 μg/g) over the acidic pH range 2-6.5. Among competitive ions, phosphate and silicate affected the most while sulfate, nitrate, chloride, fluoride, manganese, magnesium and calcium had insignificant impact. The experimental data were evaluated with Langmuir and Freundlich isotherms. The adsorption capacity for arsenate, calculated from Langmuir and Freundlich isotherms, were 5000 and 6000 µg/g, respectively at pH 4.5. The reaction kinetics followed the pseudo-second order model. The initial sorption rate (h), determined from pseudo-second order kinetic model, was 666 µg/g.min. The dynamic behaviour of the RSSCT was predicted by the HSDM model using the software FAST 2.0. From the RSSCT results, it was found that the number of bed volumes treated depends on the empty bed contact time (EBCT) as well as the initial arsenate concentration. The regeneration of spent nZVI/GAC using 0.1M NaOH was effective as it desorbed 87% of adsorbed arsenic. The solid waste can be safely disposed of in a sanitary landfill without any treatment as the concentration of leached arsenate determined by TCLP was much lower than the regulatory limit. The arsenic removal mechanism was due to the combination of electrostatic and the complex formation, either monodentate or bidentate, between As(V) and nZVI corrosion products. The results indicated that nZVI/GAC is a promising adsorbent for arsenic removal

Removal of Arsenic from Contaminated Water by Granular Activated Carbon Embedded with Nano scale Zero-valent Iron.

This study investigated the removal of arsenic from groundwater by granular activated carbon (GAC) supported nano scale zero-valent iron (nZVI). GAC supported nZVI (nZVI/GAC) composite was synthesized by hydrolyzing a Fe(III) salt on GAC, reduced by NaBH4 and dried under vacuum. Synthesized nZVI/GAC was characterized using scanning electron microscopy (SEM) along with EDS, BET surface area analysis, X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. The experimental results were produced through the batch and Rapid Small Scale Column Test (RSSCT). The adsorption depends on pH, initial concentration, and reaction time. Arsenite adsorption capacity varies from 800 to 1400 μg/g over the pH 2-11. Arsenate adsorption was higher (3000-3700 μg/g) over the acidic pH range 2-6.5. Among competitive ions, phosphate and silicate affected the most while sulfate, nitrate, chloride, fluoride, manganese, magnesium and calcium had insignificant impact. The experimental data were evaluated with Langmuir and Freundlich isotherms. The adsorption capacity for arsenate, calculated from Langmuir and Freundlich isotherms, were 5000 and 6000 µg/g, respectively at pH 4.5. The reaction kinetics followed the pseudo-second order model. The initial sorption rate (h), determined from pseudo-second order kinetic model, was 666 µg/g.min. The dynamic behaviour of the RSSCT was predicted by the HSDM model using the software FAST 2.0. From the RSSCT results, it was found that the number of bed volumes treated depends on the empty bed contact time (EBCT) as well as the initial arsenate concentration. The regeneration of spent nZVI/GAC using 0.1M NaOH was effective as it desorbed 87% of adsorbed arsenic. The solid waste can be safely disposed of in a sanitary landfill without any treatment as the concentration of leached arsenate determined by TCLP was much lower than the regulatory limit. The arsenic removal mechanism was due to the combination of electrostatic and the complex formation, either monodentate or bidentate, between As(V) and nZVI corrosion products. The results indicated that nZVI/GAC is a promising adsorbent for arsenic removal

Backcalculated Modulus of Asphalt Concrete

Asphalt Concrete (AC) is considered a spatially homogeneous material when analyzing and designing asphalt pavement. However, the modulus of AC along the wheel path and the middle of the wheel path may not be the same considering the continuous compaction by wheel loading. This study conducted monthly Falling Weight Deflectometer (FWD) tests to determine the AC modulus of a pavement section on Interstate 40 (I-40) in the state of New Mexico, USA from 2013 to 2015. The AC moduli on the wheel path, on the middle of the wheel path, on the shoulder with friction course, and on the shoulder without friction course are determined. It is mentionable that the driving lane and the shoulder have the same geometry, materials, and compaction effort. Results show that the modulus along the wheel path is almost the same as that of along the middle of the wheel path. The shoulder without friction course has a modulus greater than that of the lane AC modulus and the shoulder with the friction course. In addition, FWD backcalculated moduli at different temperatures are compared with the dynamic modulus values of the AC layer. It is found that the dynamic modulus at a loading frequency of 5 Hz is 1.7 to 1.9 times the backcalculated AC modulus

Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology

IntroductionAutomatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology.MethodsThe original U-Net model sometime has a caveat in exploring significant features. Herein, we present the Densely Convolutional Spatial Attention Network (DCSA-Net) model based on U-Net to perform the segmentation task. Furthermore, the developed model was tested on external multi-tissue dataset – MoNuSeg. To develop deep learning algorithms for well-segmenting nuclei, a large quantity of data are mandatory, which is expensive and less feasible. We collected hematoxylin and eosin–stained image data sets from two hospitals to train the model with a variety of nuclear appearances. Because of the limited number of annotated pathology images, we introduced a small publicly accessible data set of prostate cancer (PCa) with more than 16,000 labeled nuclei. Nevertheless, to construct our proposed model, we developed the DCSA module, an attention mechanism for capturing useful information from raw images. We also used several other artificial intelligence-based segmentation methods and tools to compare their results to our proposed technique.ResultsTo prioritize the performance of nuclei segmentation, we evaluated the model’s outputs based on the Accuracy, Dice coefficient (DC), and Jaccard coefficient (JC) scores. The proposed technique outperformed the other methods and achieved superior nuclei segmentation with accuracy, DC, and JC of 96.4% (95% confidence interval [CI]: 96.2 – 96.6), 81.8 (95% CI: 80.8 – 83.0), and 69.3 (95% CI: 68.2 – 70.0), respectively, on the internal test data set.ConclusionOur proposed method demonstrates superior performance in segmenting cell nuclei of histological images from internal and external datasets, and outperforms many standard segmentation algorithms used for comparative analysis