Rapid repair of severely damaged RC columns under combined loading of flexure, shear, and torsion with externally bonded CFRP

This research aimed to develop a technique to rapidly repair reinforced concrete (RC) bridge columns for emergency service restoration after severe earthquake damage has occurred. Experimental and analytical studies were conducted to study the performance and effectiveness of the proposed repair method. The experimental study included a series of 1/2-scale RC square bridge columns originally tested to failure under constant axial and increasing cyclic lateral loadings resulting in combined flexure, shear, and torsion with different torsional-to-flexural moment ratios. Using externally bonded carbon fiber reinforced polymer (CFRP) sheets, each column was repaired over a 3-day period and then retested under the same combined loading as the corresponding original column. Ruptured and/or buckled longitudinal reinforcing bars were not treated during the repair. A strength-based methodology was used to design the CFRP strengthening system to compensate for the strength loss due to the damage observed after the original test. Results indicated that the severely damaged columns were successfully repaired using the developed technique, with the exception of one column with fractured longitudinal reinforcing bars near the joint, which was only partially restored. The response of a prototype bridge structure was analyzed under earthquake loadings using OpenSees software considering different numbers and locations of repaired columns in the model. A technique was developed to model the response of the repaired column that accounted for the different damage and repair conditions along the column. The bridge models with one or more of the repaired columns were found to be capable of resisting the base shear and drift demand by the 40 ground motion records selected according to the target design spectrum, which confirmed the effectiveness of the repair --Abstract, page iv

Optimizing Parametric Factors in CIELAB and CIEDE2000 Color-Difference Formulas for 3D-Printed Spherical Objects

The current color-difference formulas were developed based on 2D samples and there is no standard guidance for the color-difference evaluation of 3D objects. The aim of this study was to test and optimize the CIELAB and CIEDE2000 color-difference formulas by using 42 pairs of 3D-printed spherical samples in Experiment I and 40 sample pairs in Experiment II. Fifteen human observers with normal color vision were invited to attend the visual experiments under simulated D65 illumination and assess the color differences of the 82 pairs of 3D spherical samples using the gray-scale method. The performances of the CIELAB and CIEDE2000 formulas were quantified by the STRESS index and F-test with respect to the collected visual results and three different optimization methods were performed on the original color-difference formulas by using the data from the 42 sample pairs in Experiment I. It was found that the optimum parametric factors for CIELAB were kL = 1.4 and kC = 1.9, whereas for CIEDE2000, kL = 1.5. The visual data of the 40 sample pairs in Experiment II were used to test the performance of the optimized formulas and the STRESS values obtained for CIELAB/CIEDE2000 were 32.8/32.9 for the original formulas and 25.3/25.4 for the optimized formulas. The F-test results indicated that a significant improvement was achieved using the proposed optimization of the parametric factors applied to both color-difference formulas for 3D-printed spherical samples.ApPEARS (Appearance Printing European Advanced Research School) European Commission 814158Spanish GovernmentEuropean Commission PID2019-107816GB-I00/SRA/10.13039/50110001103

Accurate Colour Reproduction of Human Face using 3D Printing Technology

The colour of the face is one of the most significant factors in appearance and perception of an individual. With the rapid development of colour 3D printing technology and 3D imaging acquisition techniques, it is possible to achieve skin colour reproduction with the application of colour management. However, due to the complicated skin structure with uneven and non-uniform surface, it is challenging to obtain accurate skin colour appearance and reproduce it faithfully using 3D colour printers. The aim of this study was to improve the colour reproduction accuracy of the human face using 3D printing technology. A workflow of 3D colour image reproduction was developed, including 3D colour image acquisition, 3D model manipulation, colour management, colour 3D printing, postprocessing and colour reproduction evaluation. Most importantly, the colour characterisation methods for the 3D imaging system and the colour 3D printer were comprehensively investigated for achieving higher accuracy

Identification of Hysteresis in Human Meridian Systems Based on NARMAX Model

It has been found that the response of acupuncture point on the human meridian line exhibits nonlinear dynamic behavior when excitation of electroacupuncture is implemented on another meridian point. This nonlinear phenomenon is in fact a hysteretic phenomenon. In order to explore the characteristic of human meridian and finally find a way to improve the treatment of diseases via electro-acupuncture method, it is necessary to identify the model to describe the corresponding dynamic hysteretic phenomenon of human meridian systems stimulated by electric-acupuncture. In this paper, an identification method using nonlinear autoregressive and moving average model with exogenous input (NARMAX) is proposed to model the dynamic hysteresis in human meridian. As the hysteresis is a nonlinear system with multivalued mapping, the traditional NARMAX model is unavailable to it directly. Thus, an expanded input space is constructed to transform the multi-valued mapping of the hysteresis to a one-to-one mapping. Then, the identification method using NARMAX model on the constructed expanded input space is developed. Finally, the proposed method is applied to hysteresis modeling for human meridian systems

Improving Entity Linking through Semantic Reinforced Entity Embeddings

Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity embeddings are learned from canonical Wikipedia articles and local contexts surrounding target entities. Such entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. We propose a simple yet effective method, FGS2EE, to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality. FGS2EE first uses the embeddings of semantic type words to generate semantic embeddings, and then combines them with existing entity embeddings through linear aggregation. Extensive experiments show the effectiveness of such embeddings. Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.Comment: 6 pages, 3 figures, ACL 202