Experimental Study on the Performance of Circular Concrete Columns Reinforced with GFRP under Axial Load

Corrosion of steel reinforcement in concrete structures subjected to severe environments is a big challenge and requires huge repair and maintenance costs. Glass fiber reinforced polymer (GFRP) reinforcement with its corrosion resistance and good mechanical properties is a promising solution to replace steel in such structures. This paper investigates the efficiency of using GFRP bars and spirals in concrete columns instead of conventional steel reinforcement. Five circular concrete columns of 230 mm diameter and 1500 mm height reinforced with different types and ratios of reinforcement were constructed and tested under concentric load. Test parameters included the type and ratio of the longitudinal reinforcement. The results showed that the columns reinforced with GFRP behaved in a similar way as the reference columns reinforced with steel, however, they showed slightly lower nominal capacity. It was also found that increasing the GFRP longitudinal reinforcement ratio enhanced the nominal capacity of the columns

Machine Learning Approach for Predicting Systemic Lupus Erythematosus in Oman-based Cohort

Objectives: Design a machine learning-based prediction framework to predict the presence or absence of Systemic Lupus Erythematosus (SLE) in a cohort of Omani patients. Methods: Records of 219 patients from 2006 to 2019 were extracted from SQU Hospital electronic records, 138 patients have SLE, and the remaining 81 have other rheumatologic diseases. Clinical and demographic features were analyzed to focus on the early stages of the disease. Our design implements Recursive Feature Selection (RFE) to select only the most informative features. In addition, the CatBoost classification algorithm is utilized to predict SLE and an explainer algorithm (SHAP) is applied on top of the CatBoost model to provide individual prediction reasoning which is then validated by rheumatologists. Results: CatBoost achieved an Area Under the ROC curve (AUC) score of 0.95 and a Sensitivity of 92%. Four clinical features (Alopecia, renal disorders, Acute Cutaneous Lupus, and hemolytic anemia) along with the patient’s age were shown to have the greatest contribution to the prediction by the SHAP algorithm. Conclusion: We have designed and validated an explainable framework to predict SLE patients and provide reasoning for its prediction. Our framework enables early intervention for clinicians which leads to positive healthcare outcomes. Keywords: Systemic Lupus Erythematosus; Interpretation; Machine Learning; Supervised; Clinical Decision Support System; Statistical Data; Data Analysis

Optical Tweezers Approaches for Probing Multiscale Protein Mechanics and Assembly

Multi-step assembly of individual protein building blocks is key to the formation of essential higher-order structures inside and outside of cells. Optical tweezers is a technique well suited to investigate the mechanics and dynamics of these structures at a variety of size scales. In this mini-review, we highlight experiments that have used optical tweezers to investigate protein assembly and mechanics, with a focus on the extracellular matrix protein collagen. These examples demonstrate how optical tweezers can be used to study mechanics across length scales, ranging from the single-molecule level to fibrils to protein networks. We discuss challenges in experimental design and interpretation, opportunities for integration with other experimental modalities, and applications of optical tweezers to current questions in protein mechanics and assembly

Numerical Modeling of Pervious Concrete for Optimizing Mixture Design and Pavement Thickness

Pervious concrete is a stormwater management practice with the benefits of controlling urban flooding and filtering some pollutants carried by surface runoff. However, many pervious concrete pavements fail prematurely due to inadequate thickness design to withstand the traffic loads and/or improper mixture design that leads to degraded mechanical properties, durability, permeability, and short service life. The durability and permeability issues could be addressed with a hydro-mechanical approach that balances the two performances. The structural shortcomings could be addressed with a mechanistic thickness design methodology that incorporates the fatigue life of pervious concrete, and the effect of temperature-induced stresses. The objective of this dissertation is to advance the mixture design and layer thickness design of pervious concrete by numerical modeling. The discrete element method (DEM) is used to model pervious concrete as a sphere packing bonded together by a cohesive contact model. The bond between the spheres is calibrated to reflect the effect of water-to-cement (w/c) ratio on the mechanical response of pervious concrete to uniaxial compressive loading. Then, a pore-scale finite volume model is used to simulate water flow through the DEM sphere packings and estimate the hydraulic conductivity based on the packing porosity. The hydro-mechanical approach is used to optimize the mixture composition of pervious concrete to achieve the desired compressive strength and hydraulic conductivity for varied porosity, w/c ratio, and aggregate gradation. \nThe thickness design of pervious concrete is dependent on the estimated fatigue life. Therefore, a flexural fatigue model is developed for pervious concrete based on cyclic testing. To quantify the stresses in pervious concrete slabs under various wheel loads and temperature-induced stresses, a finite element method (FEM)-based model is used. Using the FEM-based model and the developed fatigue model, a thickness design table for pervious concrete pavements with different material properties and under various traffic loads is developed.\nThe outcomes of this dissertation can be used to guide the optimization of mixture composition and pavement thickness of pervious concrete for improved and sustained drainage and mechanical performance for the required service life

Nanotechnology in Cement-Based Materials: A Review of Durability, Modeling, and Advanced Characterization

In the context of increasing applications of various nanomaterials in construction, this work reviews the renewed knowledge of nanotechnology in cement-based materials, focusing on the relevant papers published over the last decade. The addition of nanomaterials in cement-based materials, associated with their dispersion in cement composites, is explored to evaluate their effects on the resistance of cement-based materials to physical deteriorations, chemical deteriorations, and rebar corrosion. This review also examines the proposed nanoscale modeling of interactions between admixed nanomaterials and cement hydration products. At last, the recent progress of advanced characterization that employs techniques to characterize the properties of cement-based materials at the nanoscale is summarized