•The engineering properties of alkali-activated concretes (AAC) are determined.•Results are compared to existing models for portland cement concrete (PCC).•Models are proposed to predict tensile ...strength and modulus of AAC.•AAC exhibits higher tensile strength and lower Poisson’s ratio than PCC.•Alkali-activated slag concrete is much more brittle than PCC.
This paper presents an investigation into the tensile strength, modulus of elasticity, Poisson’s ratio, and stress–strain relationships of alkali-activated portland-cement-free concrete made with fly ash or ground granulated blast furnace slag (GGBFS) as the sole binder. Alkali-activated concrete is shown to be stronger in tension and have lower Poisson’s ratio than portland cement concrete. Relationships are proposed to estimate the tensile strength and modulus of elasticity based on the compressive strength of alkali-activated concrete, which are of the same form as those currently employed for portland cement concrete.
•The effects of activators and temperature on activation kinetics are investigated.•Elevated temperature and increased activator alkalinity greatly accelerate hydration.•Increased silica retards ...hydration but improves later-age strength.•The main product is C-S-H with varying levels of hydrotalcite.
The early-age reaction kinetics of alkali-activated ground granulated blast-furnace slag (GGBFS) binders as determined by in-situ isothermal calorimetry are discussed in this paper. Particular attention is paid to the effects of activator type (sodium hydroxide and sodium silicate) and concentration, as well as curing temperature (23°C and 50°C). The mechanical strength development, microstructure, and product phase composition are also discussed to provide context for the phenomena observed in the kinetics results. It is shown for both activators that elevated temperature curing greatly accelerates hydration, resulting in more rapid product formation and strength development. High-molarity sodium hydroxide activators are shown to accelerate early hydration at ambient temperature, but tend to present a barrier to advanced hydration thereby limiting the later-age strength. Elevated temperature curing is shown to remove this barrier to advanced hydration by improving solubility and diffusivity. Hydration of sodium silicate-activated slag is comparatively slow, resulting in the delayed formation of very dense products with higher mechanical strength. Increasing sodium oxide tends to accelerate hydration, resulting in improved early- and later-age strength, while increasing the silica tends to retard the reaction, resulting in slower, more complete hydration as well as improved mechanical strength.
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Conventional coagulants, like aluminium sulphate and ferric chloride, are used in potable water treatment and involve non-sustainable mining and transformation of raw materials for ...their production with costly sludge disposal. Natural coagulants are mostly obtained from bacteria, fungi, animals and plants and are classified as polysaccharide, amino-polysaccharide, poly-phenols and proteins-based substances. Plant-based coagulants extracted from Moringa oleifera, Strychnos potatorum Linn, Plantago ovate, Trigonella foenum graecum and Opuntia ficus indica are potential substitutes to chemicals mostly based on bench-scale testing. These are organic polymers and polyelectrolytes that are classified as cationic, anionic and non-ionic coagulants. This paper provides a historical and contemporary review of plant-based coagulants, their notable milestones achieved, chemistry involved, as well as bench, pilot and full scale trials, highlighting the effects of plant-based coagulants on physico-bio-chemical properties of raw water. Commercialisation constrains are also included and discussed.
SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of ...cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring. The authors present benchmark results for crack detection using SDNET2018 and a crack detection algorithm based on the AlexNet DCNN architecture. SDNET2018 is freely available at https://doi.org/10.15142/T3TD19.
•14 FE models of steel-rubber isolator with a single and multiple cores were created.•Static cyclic and dynamic time history analysis were applied.•The results suggest, isolators with multiple cores ...outperform those with a single.
Seismic base isolators are used extensively in buildings, bridges, and critical infrastructure. During a seismic event, these isolators simultaneously experience the service loads and the base shear loads. It is therefore critical to understand their mechanical response under combined loading. In previous studies, researchers designed base isolators with the assumption that the axial loading is compressive. However, the baser isolators may also experience a tensile axial load during a seismic event. Few researchers have investigated the behavior of base isolators with combined axial tensile stress and base shear. This paper uses the finite element method to model the behavior of steel-rubber base isolators under combined axial tension or compression and base shear. The effect of the magnitude and direction of the axial load is investigated for base isolators subject to 375% shear strain. The numerical models suggest that the apparent stiffness of the base isolator increases when the axial load is tensile. The influence of the number and size of rubber cores in the steel-rubber base isolator is also investigated. The results suggest that base isolators with multiple radially-distributed rubber cores outperform those with a single central rubber core.
The multiconfigurational self-consistent field theory is considered the standard starting point for almost all multireference approaches required for strongly correlated molecular problems. The ...limitation of the approach is generally given by the number of strongly correlated orbitals in the molecule, since its cost will grow exponentially with this number. We present a new multiconfigurational self-consistent field approach, wherein linear determinant coefficients of a multiconfigurational wave function are optimized via the stochastic full configuration interaction quantum Monte Carlo technique at greatly reduced computational cost, with nonlinear orbital rotation parameters updated variationally based on this sampled wave function. This extends this approach to strongly correlated systems with far larger active spaces than it is possible to treat via conventional means. By comparison with this traditional approach, we demonstrate that the introduction of stochastic noise in both the determinant amplitudes and the gradient and Hessian of the orbital rotations does not preclude robust and reliable convergence of the orbital optimization. It can even improve the ability to avoid convergence to local minima in the orbital space, and therefore aid in finding variationally lower-energy solutions. We consider the effect on the convergence of the orbitals as the number of walkers and the sampling time within the active space increases, as well as the effect on the final energy and error. The scope of the new protocol is demonstrated with a study of the increasingly strongly correlated electronic structure in a series of polycyclic aromatic hydrocarbons, up to the large coronene molecule in a complete active space of 24 π electrons in 24 orbitals, requiring only modest computational resources.
By applying the Gibbs equation to the bulk binding isotherms and surface composition of the air–water (A–W) interface in polyelectrolyte–surfactant (PE–S) systems, we show that their surface behavior ...can be explained semiquantitatively in terms of four concentration regions, which we label as A, B, C, and D. In the lowest-concentration range A, there are no bound PE–S complexes in the bulk but there may be adsorption of PE–S complexes at the surface. When significant adsorption occurs in this region, the surface tension (ST) drops with increasing concentration like a simple surfactant solution. Region B extends from the onset of bulk PE–S binding to the end of cooperative binding, in which the slow variation of surfactant activity with cooperative binding means that the ST changes relatively little, although adsorption may be significant. This leads to an approximate plateau, which may be at high or low ST. Region C starts where the binding in the bulk complex loses its cooperativity leading to a rapid change of surfactant activity with the total concentration. This, combined with significant adsorption, often leads to a sharp drop in ST. Region D is where precipitation and redissolution of the bulk PE–S complex occur. ST peaks may arise in region D because of loss of the solution complex that matches the value of the preferred surface stoichiometry, which seems to have a well-defined value for each system. The analysis is applied to the experimental systems, sodium polystyrene sulfonate–alkyltrimethylammonium bromides and poly(diallyldimethyl chloride)–sodium alkyl sulfates, with and without the added electrolyte, and includes data from bulk binding isotherms, phase diagrams, aggregation behavior, and direct measurements of the surface excess and stoichiometry of the surface. The successful fits of the Gibbs equation to the data confirm that the surfaces in these systems are largely equilibrated.
•Investigating the performance of six edge detectors for concrete crack detection.•Studying the performance of a DCNN trained in three modes to detect the same cracks.•Comprehensive comparison ...between the edge detectors and the DCNNs.•Proposing a new hybrid crack detector by combining the DCNN and the edge detector.•The hybrid method had 24 times less noise than the least noisy edge detector.
This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they produced residual noise in the final binary images. The best of these methods was useful in detecting cracks wider than 0.1 mm. DCNNs were used to label images, and accurately labeled them with 99% accuracy. In transfer learning mode, the network accurately detected about 86% of cracked images. DCNNs also detected much finer cracks than edge detection methods. In fully trained and classifier modes, the network detected cracks wider than 0.08 mm; in transfer learning mode, the network was able to detect cracks wider than 0.04 mm. Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process. These results show significant promise for future adoption of DCNN methods for image-based damage detection in concrete. To reduce the residual noise, a hybrid method was proposed by combining the DCNN and edge detectors which reduced the noise by a factor of 24.
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