A Semantic Information Management Approach for Improving Bridge Maintenance based on Advanced Constraint Management

Bridge rehabilitation projects are important for transportation infrastructures. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success bridge rehabilitation projects, on the other hand improves information management practices in the construction industry

Engineering brain : metaverse for future engineering

The past decade has witnessed a notable transformation in the Architecture, Engineering and Construction (AEC) industry, with efforts made both in the academia and industry to facilitate improvement of efficiency, safety and sustainability in civil projects. Such advances have greatly contributed to a higher level of automation in the lifecycle management of civil assets within a digitalised environment. To integrate all the achievements delivered so far and further step up their progress, this study proposes a novel theory, Engineering Brain, by effectively adopting the Metaverse concept in the field of civil engineering. Specifically, the evolution of the Metaverse and its key supporting technologies are first reviewed; then, the Engineering Brain theory is presented, including its theoretical background, key components and their inter-connections. Outlooks of this theory’s implementation within the AEC sector are offered, as a description of the Metaverse of future engineering. Through a comparison between the proposed Engineering Brain theory and the Metaverse, their relationships are illustrated; and how Engineering Brain may function as the Metaverse for future engineering is further explored. Providing an innovative insight into the future engineering sector, this study can potentially guide the entire industry towards its new era based on the Metaverse environment

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

Ecosystem visualization and analysis of Chinese prefabricated housing industry

Prefabricated housing has proven to achieve high quality, reduce costs and improve housing environmental performance. While prefabricated housing has been widely constructed in many jurisdictions, it is still in its infancy in China. However, the prefabrication sector in China is in transition as the macro environment is changing and new participants are expected to enter architecture, engineering and construction (AEC) industry who will have to work cooperatively as well as competitively with the existed companies. Such changes and strategic activities have impacts on every participant within the ecosystem. A better understanding of the whole industry as well as the participants’ strategic positioning will help companies develop their survival strategies. This paper aims to establish the prefabricated housing ecosystem in China based on the business ecosystem theory and to analyze the interrelationships among the major participants. A conceptual model of the ecosystem is established through literature review. Subsequently, the social network analysis (SNA) approach is employed to quantitatively analyze the strategic relationships between property developers and contractors who have adopted prefabrication in their residential projects. Finally, Node XL software is used for visualization and data analysis. Through the SNA measurements, the top 3 property developers and contractors are identified and several clusters are uncovered, which suggests a cooperation tendency among local actors

On the Optimization of a Centrifugal Maglev Blood Pump Through Design Variations

Centrifugal blood pumps are usually designed with secondary flow paths to avoid flow dead zones and reduce the risk of thrombosis. Due to the secondary flow path, the intensity of secondary flows and turbulence in centrifugal blood pumps is generally very high. Conventional design theory is no longer applicable to centrifugal blood pumps with a secondary flow path. Empirical relationships between design variables and performance metrics generally do not exist for this type of blood pump. To date, little scientific study has been published concerning optimization and experimental validation of centrifugal blood pumps with secondary flow paths. Moreover, current hemolysis models are inadequate in an accurate prediction of hemolysis in turbulence. The purpose of this study is to optimize the hydraulic and hemolytic performance of an inhouse centrifugal maglev blood pump with a secondary flow path through variation of major design variables, with a focus on bringing down intensity of turbulence and secondary flows. Starting from a baseline design, through changing design variables such as blade angles, blade thickness, and position of splitter blades. Turbulent intensities have been greatly reduced, the hydraulic and hemolytic performance of the pump model was considerably improved. Computational fluid dynamics (CFD) combined with hemolysis models were mainly used for the evaluation of pump performance. A hydraulic test was conducted to validate the CFD regarding the hydraulic performance. Collectively, these results shed light on the impact of major design variables on the performance of modern centrifugal blood pumps with a secondary flow path

Simultaneous treatment of phosphorus and fluoride wastewater using acid-modified iron-loaded electrode capacitive deionization: Preparation and performance

Here, capacitive deionization technology (CDI) using modified activated carbon fiber felt (ACF) electrodes was proposed to provide a new strategy for the challenge of simultaneous phosphorus and fluoride wastewater treatment. The acid-modified iron-loaded ACF (A@Fe-ACF) was obtained by modifying ACF through a two-step impregnation method. After the modification, the oxygen-containing functional groups on ACF increased and provided more adsorption sites. The electron transfer efficiency on the A@Fe-ACF was increased by introducing Fe and synergistically promoted the adsorption of phosphorus and fluorine. Results showed that the removal efficiencies of total phosphorus (TP) and total fluorine (TF) in wastewater reached 89.4% and 85% under optimal conditions (voltage intensity 1.5 V, pH 7, plate spacing 1 cm), while the adsorption mechanism of phosphorus and fluorine was dominated by chemical adsorption. Meanwhile, A@Fe-ACF electrode has good recyclability and stability after five cycles

Less Conserved LRRs Is Important for BRI1 Folding

Brassinosteroid insensitive 1 (BRI1) is a multidomain plant leucine-rich repeat receptor-like kinase (LRR-RLK), belongs to the LRR X subfamily. BRI1 perceives plant hormone brassinosteroids (BRs) through its extracellular domain that constitutes of LRRs interrupted by a 70 amino acid residue island domain (ID), which activates the kinase domain (KD) in its intracellular domain to trigger BR response. Thus, the KD and the ID of BRI1 are highly conserved and greatly contribute to BR functions. In fact, most bri1 mutants are clustered in or surrounded around the ID and the KD. However, the role of the less conserved LRR domains, particularly the first few LRRs after the signal peptide, is elusive. Here, we report the identification of a loss-of-function mutant bri1-235 that carries a mutation in the less conserved fourth LRR of BRI1 extracellular domain in Arabidopsis. This mutant had a base alteration from C to T, resulting in an amino acid substitution from serine to phenylalanine at the 156th position of BRI1. Compared with the wild-type plants, bri1-235 exhibited round leaves, prolonged life span, shorter stature, and approximately normal fertility under light conditions. The bri1-235 mutant was less sensitive to exogenous brassinolide under normal conditions. Importantly, both wild-type BRI1 expression and a sbi1 mutant that activates BRI1 rescued bri1-235 and resembled the wild type. Furthermore, bri1-235 protein was localized in endoplasmic reticulum rather than plasma membrane, suggestive of a cause for reducing BR sensitive in bri1-235. Taken together, our findings provide an insight into the role of the less conserved LRRs of BRI1, shedding light on the role of LRRs in a variety of LRR-RLKs that control numerous processes of plant growth, development, and stress response

Random forest based optimal feature selection for partial discharge pattern recognition in HV cables

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. Firstly, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, then a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus

Image Representation by Integrating Curvature and Delaunay Triangulation

International audienceA method for representing images by integrating curvatures and Delaunay triangulations is presented in this paper. The images to be represented include both range images and illuminated images. A plane or triangle is most often used to represent a surface in the images. The Delaunay triangulation is adopted in this paper for its numerical stability in computation. By segmenting the images in some regions before triangulation, the number of triangles represented will be reduced largely. Gaussian and mean curvatures are used for the segmentation and B-spline surface smoothing is performed before the curvature computation. The experimental results on a number of range and illuminated images are given. The results show that the integrating method for representing both range and illuminated images is effective

Estimating the Fundamental Matrix Based on a New Constraint

International audienceA new method is developed to estimate fundamental matrix (F matrix). We first find two parameters in the 8-parameter F model by the new constraint developed in this paper, the two parameters are the affine coordinates of an epipole. Then we obtain the rest 6 parameters by solving a set of linear equations. Finally, our method is tested with many real images and compared with the 8-point method and some iterative algorithms, the results show that our method has many advantages, such as the obvious geometrical meaning, the fewer matching pairs needed for calculation and high accuracy F matrice