Development of Non-Proprietary Ultra-High Performance Concrete Mixtures

The development of non-proprietary Ultra-High Performance Concrete (UHPC) is one way to reduce the initial cost of construction. However, workability is a major issue for which such mixtures are not practical in field conditions. Ultra-high performance cannot be achieved in field conditions if the concrete is not placed, finished, and compacted properly during placement. In this research, six UHPC mixtures were developed (three with steel fibers and three without fibers) using materials which are readily available on the local marketplace with water-to-cementitious materials ratios ranging between 0.17 to 0.30. The workability was determined using standard ASTM flow table apparatus, and specimens were prepared to determine compressive strength, splitting tensile strength, and permeable porosity. Flow table test exhibited flow values greater than 250 mm. Such high workability of the mixtures was achieved by optimizing the silica fume content and water reducing admixture dosage. These mixtures exhibited compressive strengths greater than 120 MPa and splitting tensile strengths greater than 5.10 MPa in both ambient and elevated curing temperatures. Results indicated that UHPC can be produced with a water-to-cementitious materials ratio as high as 0.30. Steel fibers helped to increase splitting tensile strength due to fiber-matrix interactions. Very low permeable porosity (1.7-16.7%) was observed which indicates superior durability due to the significant reduction of ingress of deleterious ions

Review: Inelastic Constitutive Modeling: Polycrystalline Materials

This article provides a literature review that details the development of inelastic constitutive modeling as it relates to polycrystalline materials. This review distinguishes between inelastic constitutive models that account for nonlinear behavior at the microstructural level, time-independent classic plasticity models, and time-dependent unified models. Particular emphasis is placed on understanding the underlying theoretical framework for unified viscoplasticity models where creep and classical plasticity behavior are considered the result of applied boundary conditions instead of separable rates representing distinct physical mechanisms. This article establishes a clear understanding of the advantages of the unified approach to improve material modeling. This review also discusses recent topics in constitutive modeling that offer new techniques that bridge the gap between the microstructure and the continuum

Reduced Bacterial Counts from a Sewage Treatment Plant but Increased Counts and Antibiotic Resistance in the Recipient Stream in Accra, Ghana-A Cross-Sectional Study.

Wastewater treatment plants receive sewage containing high concentrations of bacteria and antibiotics. We assessed bacterial counts and their antibiotic resistance patterns in water from (a) influents and effluents of the Legon sewage treatment plant (STP) in Accra, Ghana and (b) upstream, outfall, and downstream in the recipient Onyasia stream. We conducted a cross-sectional study of quality-controlled water testing (January-June 2018). In STP effluents, mean bacterial counts (colony-forming units/100 mL) had reduced E. coli (99.9% reduction; 102,266,667 to 710), A. hydrophila (98.8%; 376,333 to 9603), and P. aeruginosa (99.5%; 5,666,667 to 1550). Antibiotic resistance was significantly reduced for tetracycline, ciprofloxacin, cefuroxime, and ceftazidime and increased for gentamicin, amoxicillin/clavulanate, and imipenem. The highest levels were for amoxicillin/clavulanate (50-97%) and aztreonam (33%). Bacterial counts increased by 98.8% downstream compared to the sewage outfall and were predominated by E. coli, implying intense fecal contamination from other sources. There was a progressive increase in antibiotic resistance from upstream, to outfall, to downstream. The highest resistance was for amoxicillin/clavulanate (80-83%), cefuroxime (47-73%), aztreonam (53%), and ciprofloxacin (40%). The STP is efficient in reducing bacterial counts and thus reducing environmental contamination. The recipient stream is contaminated with antibiotic-resistant bacteria listed as critically important for human use, which needs addressing

SHEAR FORCE DISTRIBUTION IN STEEL BRIDGE GIRDERS

Most engineers use empirical formulas stated in the American Association of State Highway and Transportation Officials (AASHTO) code to design various bridge projects. Recent research suggests that the distribution factors which result from the American Association of State Highway and Transportation Officials Load Resistance Factor Design (AASHTO LRFD) are overestimated or underestimated in various load cases when compared with finite element analysis. This paper will present a comparison between the shear live load distribution factors of a simple span concrete slab on steel girders bridge based on the current AASHTO LRFD equations and finite element analysis. The details of the bridge design example that will be used for this study will be discussed further in this paper

Detwinning of Preloaded Martensite in Shape Memory Alloys and Its Effect on the Cyclic Behavior of NiTi Cylindrical Actuators

© 2017, © The Author(s) 2017. A multi-mechanism material model is used to investigate the effect of the degree of martensitic detwinning/reorientation occurring at the end of mechanical preloading, on the performance of shape memory tubular and solid cylindrical actuators. A high-temperature ternary Ni50.3Ti29.7Hf20 alloy, showing almost complete transformation behavior, and an ordinary binary Ni49.9Ti50.1 material, depicting incomplete martensitic transformation, are used. The results indicated a clear correlation between the shape memory cyclic actuation behavior and the martensitic deformation character of the selected alloy. More specifically, while the actuation strokes produced by the Ni50.3Ti29.7Hf20 systems consistently followed a direct pattern for the different geometries and torque magnitudes, the results for the Ni49.9Ti50.1 cases indicated a behavior that is counterintuitive to what may be expected under pure mechanical loading conditions. In particular, when subjected to the same, high preload torques, Ni49.9Ti50.1 solid actuator under lesser stresses generated higher twist strokes than a tubular counterpart experiencing higher stresses

Cyclic Pseudoelastic Training and Two-way Shape Memory Behavior of a NiTi Alloy with Small Irrecoverable Plastic Strains:Numerical Modeling

Novel applications of shape memory alloys (SMAs) in various fields of medicine and engineering necessitate theoretical models that can capture non-inherent material behaviors such as the two-way shape memory effect (TWSME). Most often, this requires the use of completely different sets of models or parameters to distinguish the virgin material exhibiting the fundamental behaviors like superelasticity (also known as pseudoelasticity), pseudoplasticity, and one-way shape memory effect, from the trained alloy showing the TWSME. Although using a single model (or set of parameters) can provide much insight on the changes within the material during various loading paths and histories from the initial virgin state to the trained state, it is quite challenging. In this study, an attempt was made to use a developed general multi-mechanism material model and a single set of parameters to predict both the inherent pseudoelastic behavior and non-intrinsic two-way shape memory effect characteristics of a NiTi shape memory alloy. To this end, several simulations were carried out to investigate the effect of pseudoelastic training conditions on the corresponding actuation strain, cyclic stability, and transformation temperatures of the NiTi under zero-load TWSME. Whilst previous studies considered external indicators such as the magnitude of plastic or irrecoverable strain as a prerequisite for TWSME, the present model focused primarily on internal stresses. Based on the magnitude of the zero-load actuation strain (measured from the trained material under no external load), the model results indicated that the magnitude of the pseudoelastic training strain (in comparison to the training temperature, and the number of training cycles) has a pronounced effect on the cyclic TWSME actuation characteristics. Moreover, observations from the internal state (hidden) variables of the model, representing the intrinsic residual stress state in the trained material (under zero observable/external stress) showed the possibility for an SMA to generate TWSME even if there is no significant accumulation of permanent strains. (C) 2021 Elsevier Ltd. All rights reserved

Artificial Neural Network Algorithms to Predict the Bond Strength of Reinforced Concrete: Coupled Effect of Corrosion, Concrete Cover, and Compressive Strength

Degradation of the bond between reinforcement steel bars and concrete poses a huge challenge to the design of sustainable infrastructure. In this study, an initial effort was made to develop and apply Artificial Neural Network (ANN) models to predict the bond strength between steel reinforcement and concrete. To assess the efficiency of ANN under a case of limited experimental data, the ANN models were activated through Softplus, Rectified Linear unit (ReLU), or Sigmoid functions and their results were compared. The experimental/test data used in the modeling study only covered corrosion levels from 0 to 20 % of the reinforcement bars\u27 weight, concrete compressive strengths of 23 and 51 MPa, and concrete covers ranging between 15 and 45 mm. A comparison was made between the bond strength values predicted by the ANN models, linear/non-linear statistical regression equations, and other analytical equations available in the literature. The model results indicated that the bond strength was predominantly affected by the level of corrosion (in comparison to the other parameters). Moreover, the ANN(Softplus) model with a mean squared error (J) of 2.89 and a coefficient of determination (R2) of 96 % demonstrated a more accurate prediction of the bond strength in comparison to the ANN(Sigmoid), ANN(ReLu), and statistical regression models

Hybrid Battery Thermal Management System with NiTi SMA and Phase Change Material (PCM) for Li-ion Batteries

Poor heat dissipation and thermal runaway are most common in batteries subjected to fast charge or discharge and forced to work in hot or subzero ambient temperatures. For the safe operation of lithium-ion batteries throughout their lifecycle, a reliable battery thermal management system (BTMS) is required. A novel hybrid BTMS with a nickel-titanium (NiTi) shape memory alloy (SMA) actuated smart wire and phase change material (PCM) with expanded graphite (EG) is proposed in this study. A lumped electrochemical-thermal battery model is developed to analyze the efficiency of the proposed hybrid BTMS. The multiphysics BTMS is investigated by discharging at various electrical currents in both off-modes (inactivated SMA) and on-modes (activated SMA). Under on-mode BTMS operation, temperature elevation is reduced by 4.63 degrees C and 6.102 degrees C during 3 C and 5 C discharge, respectively. The proposed hybrid BTMS can be considered a competitive alternative for use in electrical vehicles due to its smart, compact, safe, and efficient performance in both cold and hot environments

Additively Manufactured Custom Soft Gripper with Embedded Soft Force Sensors for an Industrial Robot

Soft robotic grippers are required for power grasping of objects without inducing damage. Additive manufacturing can be used to produce custom-made grippers for industrial robots, in which soft joints and links are additively manufactured. In this study, a monoblock soft robotic gripper having three geometrically gradient fingers with soft sensors was designed and additively manufactured for the power grasping of spherical objects. The monoblock structure design reduces the number of components to be assembled for the soft gripper, and the gripper is designed with a single cavity to enable bending by the application of pneumatic pressure, which is required for the desired actuation. Finite element analysis (FEA) using a hyperelastic material model was performed to simulate the actuation. A material extrusion process using a thermoplastic polyurethane (TPU) was used to manufacture the designed gripper. Soft sensors were produced by a screen printing process that uses a flexible material and ionic liquids. The grasping capability of the manufactured gripper was experimentally evaluated by changing the pneumatic pressure (0-0.7 MPa) of the cavity. Experimental results show that the proposed monoblock gripper with integrated soft sensors successfully performed real-time grasp detection for power grasping