Hasil untuk "Bridge engineering"

P. Krantz, M. Kjaergaard, F. Yan et al.

The aim of this review is to provide quantum engineers with an introductory guide to the central concepts and challenges in the rapidly accelerating field of superconducting quantum circuits. Over the past twenty years, the field has matured from a predominantly basic research endeavor to one that increasingly explores the engineering of larger-scale superconducting quantum systems. Here, we review several foundational elements -- qubit design, noise properties, qubit control, and readout techniques -- developed during this period, bridging fundamental concepts in circuit quantum electrodynamics (cQED) and contemporary, state-of-the-art applications in gate-model quantum computation.

Riccardo Miotto, Fei Wang, Shuang Wang et al.

A. Gawer

An integrative framework is proposed to advance management research on technological platforms, bridging two theoretical perspectives: economics, which sees platforms as double-sided markets, and engineering design, which sees platforms as technological architectures. While the economic perspective informs our understanding of platform competition, the engineering design perspective informs our view of platform innovation. The article argues that platforms can be usefully conceptualized as evolving organizations or meta-organizations that: (1) federate and coordinate constitutive agents who can innovate and compete; (2) create value by generating and harnessing economies of scope in supply or/and in demand; and (3) entail a modular technological architecture composed of a core and a periphery. In support of this conceptualization, a classification system is presented, indicating that technological platforms appear in a variety of organizational forms: within firms, across supply chains, and across industry innovation ecosystems. As an illustration, the framework is then applied to derive a simple model highlighting patterns of interaction between platform innovation and competition, yielding hypotheses that could be tested empirically by future scholars.

T. Bielecki, M. Rutkowski

William C. Y. Lee

Hainan Sun, Xiaomin Xu, Hyuncheol Kim et al.

Electrochemical water splitting represents one of the most promising technologies to produce green hydrogen, which can help to realize the goal of achieving carbon neutrality. While substantial efforts on a laboratory scale have been made for understanding fundamental catalysis and developing high‐performance electrocatalysts for the two half‐reactions involved in water electrocatalysis, much less attention has been paid to doing relevant research on a larger scale. For example, few such researches have been done on an industrial scale. Herein, we review the very recent endeavors to bridge the gaps between fundamental research and industrial applications for water electrolysis. We begin by introducing the fundamentals of electrochemical water splitting and then present comparisons of testing protocol, figure of merit, catalyst of interest, and manufacturing cost for laboratory and industry‐based water‐electrolysis research. Special attention is paid to tracking the surface reconstruction process and identifying real catalytic species under different testing conditions, which highlight the significant distinctions of corresponding electrochemical reconstruction mechanisms. Advances in catalyst designs for industry‐relevant water electrolysis are also summarized, which reveal the progress of moving the practical applications forward and accelerating synergies between material science and engineering. Perspectives and challenges of electrocatalyst design strategies are proposed finally to further bridge the gaps between lab‐scale research and large‐scale electrocatalysis applications.

Dongdong Wang, Deblin Jana, Yanli Zhao

ConspectusNanozymes, which integrate the advantages of both nanomaterials and natural enzymes, have accumulated enormous research interest over the past decades because of the opportunity they provide to appreciate and further cultivate artificial enzymes with comparable properties. By mimicking the coordination environments of the catalytic sites in natural enzymes, nanozymes with confined nanostructures can serve as substitutes in many catalytic processes with comparable activity and robust stability even in harsh conditions. Since the pioneering report about peroxidase-mimicking ferromagnetic nanoparticles in 2007, nanozymes have been developed as specialized for nanomaterials with intrinsic enzyme-mimicking property. With the rapid development in nanoscience and nanotechnology, nanomaterials with superior advantages, such as large-scale production, desired activity, and robust stability, can bridge the natural enzymes with nanozymes.Metal-organic frameworks (MOFs) and their derivatives hold great promise to serve as direct surrogates of conventional enzymes for enzymatic reactions. According to their chemical nature, MOF-based nanozymes can be divided into three main categories: pristine MOFs, enzyme-encapsulated MOF composites, and MOF-based derivatives. Due to the versatility of metallic nodes and bridging linkers together with the feasibility of postsynthetic engineering and modification, MOFs and their derivatives are envisioned as one of the most appropriate surrogates for this purpose. Using MOFs as precursors or sacrificial templates, multiple MOF-based derivatives including carbon-based nanomaterials (e.g., heteroatom-doped carbon or carbon with M-N-C moiety), metal oxide/carbon nanoparticles, and metal/carbon nanomaterials can be rationally synthesized through one-step direct carbonization/oxidation or indirect post-synthesis treatments of MOFs (e.g., bridging linker-exchange and metallic node-doping). Compared with existing nanozymes, MOF-based derivatives open up a new avenue for constructing mesoporous nanozymes. In this way, the intrinsic mesoporous properties of MOFs can still be maintained, while the stability and activity can be greatly improved. In this Account, we highlight some important research advances in MOF-based derivatives (including M-N-C moieties (M = single metal atom), metal oxide/carbon, metal/carbon, and MOF derivatives obtained through postsynthetic linker exchange and metal doping strategies) with enzyme-mimicking activity. We also demonstrate that, through integrating physicochemical properties of mesoporous nanomaterials and enzymatic activities of natural enzymes, MOF-derived nanozymes can provide multifunctional platforms in biomedical fields such as antibacterial agents, biosensors, imaging, cancer therapy, and environmental protection. Finally, we propose future design principles and possible research approaches for deeper understanding of mechanisms, thus pointing out future research directions to offer more opportunities for the conventional enzyme-engineering industry.

S. Qiu, G. Zhu

Mamun Or Rashid, Md. Mosarrof Hossen, Mohammad Nashbat et al.

Distraction can cause delayed decision-making and slower awareness, posing significant risks in driving. For reliable driving systems, continuous monitoring of driver behavior is essential to mitigate the impact of distractions. Current strategies for distraction detection widely rely on machine learning models, but the non-linear relationships among various data modalities complicate the identification of optimal combinations. To address these challenges, we propose a novel model, Self-DSNet, for efficient driving distraction detection. The proposed Self-DSNet model utilizes Self-Organizing Neural Network (Self-ONN) layers to enhance complex pattern learning within the data. We employed a publicly available multimodal dataset encompassing three data categories: physiological, vehicle dynamics, and vision-based data. Our approach aims to identify the normal state and three types of distractions: cognitive, emotional, and sensorimotor. The model was evaluated using both single-modality and combined-modality data, focusing on binary classification to distinguish between distracted and non-distracted driving states. The Self-DSNet model demonstrated an impressive accuracy of 94.23% when using vision-based data alone. Incorporating additional physiological data, such as heart rate and breathing rate, alongside vehicle dynamics data, such as steering behavior, further enhanced the model’s performance. The combined data approach achieved a 95.13% accuracy in detecting driving distractions. Specifically, the binary classification yielded a 96.58% accuracy with vision-based data, which increased to 97.31% when steering, breathing rate, and heart rate data were included. Our approach significantly outperformed state-of-the-art methods in terms of classification accuracy. The proposed Self-DSNet model offers a robust solution for driving distraction detection by effectively leveraging multimodal data and enhancing complex pattern recognition through the Self-ONN layers. The model’s high accuracy rates underscore its potential for improving driving safety by providing reliable and continuous monitoring of driver behavior. Future research may focus on real-time implementation and the integration of additional data sources to further refine and validate the model’s effectiveness in diverse driving scenarios.

Xinzhe Li, Ganyou Jiang, Naishuang Wang et al.

The early shrinkage-deformation and mechanical property evolution of gradient-structured composites in extreme environments are still insufficient. The paper prepared ordinary Portland cement-alkali-activated slag (OPC-AAS) and ordinary Portland cement-alkali-activated metakaolin (OPC-AAMK) gradient-structured composite by stacking cement and alkali-activated materials together. The effects of temperature difference cycling and wetdry cycling extremes on the early shrinkage strain and splitting strength of OPC-AAS and OPC-AAMK composites, as well as the structure of the bond interface and the micromorphology of the hydration products, were comparatively analyzed. The results demonstrated that the temperature difference cycling affected the early deformation and bond strength of the gradient-structured composite interfaces more significantly than the dry-wet cycling. The maximum expansion strains of OPC-AAS and OPC-AAMK were 1,130.88 μm and 1,399.25 μm, respectively, under the effect of temperature difference cycling; the splitting strengths of OPC-AAS and OPC-AAMK after three cycles of temperature difference cycling were reduced by 26.37% and 31.32%, respectively, compared with that after three cycles of wet-dry cycling. In addition, the OPC-AAS composites showed better interfacial bonding properties after extreme environmental cycling compared to the OPC-AAMK composites. The early splitting strengths under the two extreme environmental effects increased and then decreased, and the maximum splitting strengths of OPCAAS were 2.66 MPa and 3.65 MPa under the temperature difference cycling and dry-wet cycling, respectively, which were 5.14% and 35.69% higher than those of OPC-AAMK, respectively. Scanning electron microscopy (SEM) characterization analysis showed that the temperature difference cycling resulted in more severe product decomposition of the AAMK cementitious material, and obvious cracks and holes appeared at the bonding interface of OPC-AAMK. This study provides some references for the optimal design of the early shrinkage-deformation properties and mechanical properties of gradient-structured composites under extreme environments as well as the assessment of service life.

W. Y. Teoh, J. Scott, R. Amal

Xinyu Huo, Peiwen Wang, Sihui Wang et al.

Numerous studies have indicated that the incorporation of chopped fibres can significantly improve the mechanical properties and durability of concrete. To further explore the effect of chopped basalt fibres, PVA fibres and their hybrid fibres on the improvement of concrete mechanics and impermeability, mechanical property and impermeability tests of chopped basalt fibres/PVA fibres of different lengths and hybrid ratios mixed with concrete of disparate benchmark strengths were carried out. By comparing the characteristic values of each group and their variation rules, the optimal length and hybrid ratio of the fibres were determined. The results show that the enhancement effect of chopped basalt fibres of different lengths on the mechanical and impermeability properties of concrete is different, showing a trend of first increasing and then decreasing. The enhancement effect of chopped basalt fibres on the mechanical and impermeability performance of concrete with different benchmark strengths is better than that of PVA fibres. Compared with those of the C30 plain concrete, the mechanical and impermeability properties of the chopped basalt-PVA hybrid fibre concrete significantly improve to varying degrees. When the proportion of chopped basalt fibres is greater than or equal to the proportion of PVA fibres, a positive hybrid effect on the mechanical and impermeability performance of concrete with the same benchmark strength occurs. With increasing benchmark strength, the enhancement effect of the two types of fibres on the mechanics and impermeability of the concrete decreases. The attenuation range of PVA fibres is greater than that of chopped basalt fibres, and even a reduction in the mechanical and impermeability properties of concrete occurs. This results in the enhancement effect of chopped basalt-PVA hybrid fibres on concrete mechanics as well as a significant decrease in the impermeability. The improvement rates of the compressive performance and impermeability of concrete by a single or a hybrid of the two fibres are highly similar, and the enhancement effect of the fibre compressive performance is significantly greater than that of the impermeability.

Ying Wu, Yigang Wang, Hongbing Liu et al.

Abstract Bridge construction collapse is one of the most common bridge safety accidents. At present, evaluation results are often affected by the ability and experience of the assessor. Therefore, it is difficult to quickly, accurately and effectively evaluate the risk in the process of bridge construction. Moreover, key factors that can prevent accidents can hardly find from the existing bridge construction safety management and evaluation method. This paper analyzes and classifies the artificial and environmental risk factors that affect the bridge construction stage, and establishes 26 risk factors in 5 categories according to the characteristics of bridge construction and the actual situation of the project. Random forest (RF) algorithm is a non-parametric machine learning method based on decision tree, which does not need to be scored by experts in advance and avoids the influence of subjective factors. Compared with other analysis methods, random forest algorithm has the advantages of accurate and robust risk assessment results. Based on the advantages of random forest algorithm and the characteristics of bridge construction risk, this paper uses random forest algorithm to evaluate the bridge construction risk, and ranks the importance of indicators, and identify the index that has a greater influence on the risk. In order to verify the applicability and feasibility of the proposed method, a typical urban complex pedestrian bridge was taken as an example for actual engineering evaluation and verification. The results obtained are basically consistent with the actual risk assessment results of the pedestrian bridge.

Jieli LI, Jiaguo ZHANG, Quansheng CHEN et al.

Taking Yunnan Tabaiyi Tunnel as the background of the research project, as the most complex tunnel in the whole line, its complexity is manifested as follows: the tunnel site area is a tectonic dissolution and erosion area, the geological condition is variable, crossing a number of faults and fracture zones, the local passage is water-rich, the rock weathering is serious, and the influence of tectonic stress is intense, etc. The original supporting action has resulted in large deformation of the initial support, deformation of the steel arch, and sudden mud and water surge. Under the original support, there were large deformation of the initial support, deformation of the steel arch frame, and sudden mud and water influx. The deformation and damage mechanism of the surrounding rock was studied by field investigation, rock structure analysis and numerical simulation. The surrounding rock is strongly—fully and moderately weathered metamorphic sandstone sandwiched with schist and slate mainly. Scanning electron microscopy observation of the rock body shows that the schist structure is obvious, the rock body structure is relatively loose, and the pores are more developed. The clay mineral content in the rocks is high, and the main components are illite and chlorite. The regional ground stress is mainly dominated by tectonic stress, and the groundwater in the tunnel site area is controlled by lithology, tectonics, topography and geomorphology, etc. The recharge of groundwater is closely related to rainfall, etc. The groundwater is mainly stored in the form of pore water of the loose accumulation layer, rock fissure water and karst water. Under the implementation of NPR anchor cable control system, the effect of large deformation management is remarkable, and the large deformation of surrounding rock is effectively controlled within 300 mm. In order to ensure the safety of the project and improve the construction efficiency, the optimization design of NPR anchor network and three-dimensional steel arch coupling support is carried out, and numerical simulation of several NPR anchor cable control systems is carried out using FLAC3D and PFC3D software to establish the NPR anchor network unit and double-layer three-dimensional steel arch with constant resistance to large deformation effect, and the deformation diagrams of the surrounding rock and the displacement diagrams of the support structure obtained from the simulation under the comparison and analysis of the various schemes are then carried out. Field test, the meter-level asymmetric deformation control results and numerical solutions for analysis and comparison, that the NPR anchor network and three-dimensional steel arch support system can solve the water-rich peripheral rock, mud, broken and local high-pressure water influx of engineering and technical problems, to get the NPR anchor cable as the core of the different support process of the coupled support parameters of the tunnel peripheral convergence under the deformation of the evolution of the law.

Xudong Wang, Changming Hu, Jing Liang et al.

To enhance the safety management of steel-truss-bridge construction, an evaluation method based on the improved DEMATEL–ISM was proposed to analyze the risk factors involved in such construction. Decision Making Trial and Evaluation Laboratory (DEMATEL) is a method for systematic factor analysis that utilizes graph-theory and -matrix tools, allowing for the assessment of the existence and strength of relationships between elements by analyzing the logical and direct impact relationships among various elements in a system. The distinctive feature of Interpretative Structural Modeling (ISM) is the decomposing of complex systems into several subsystems (elements) and constructing the system into a multi-level hierarchical structural model through algebraic operations. Specifically, triangular fuzzy numbers are introduced initially to improve the direct influence matrix in the DEMATEL method, thereby reducing the subjectivity of expert evaluations. The degree of influence, influenced degree, centrality degree, and causality degree of each influencing factor are determined and ranked based on the above analysis. In response to the characteristics of top-push construction, 20 key factors were selected from four aspects: “human, material, environment, and management”. The top five identified influencing factors are displacement during pushing (X10), safety-management qualification (X18), local buckling (X14), overturning of steel beams (X13), and collision with bridge piers during guide beam installation (X7). Subsequently, corresponding solutions were proposed for different influencing factors. The results of the study offer targeted measures to enhance the safety management of steel truss bridge construction and provide a reference for accident prevention.

Guilin Gong, Shaowu Zeng, Jingjing Gao et al.

All along, safety accidents in the field of bridge construction have continued to arise, costing society's development financial and material resources and taking away people’s personal safety, turning into an urgent need for safety managers in the field to solve the subject research. Researchers look forward to continued innovative exploration to find new ways of working safely and using them in the construction industry. On this basis, BIM virtual scenes are introduced to create a universal scene specification dataset in the field of bridge construction and a dataset of unsafe potential hazard characteristics in the field of bridge construction. Nanobots are artificial intelligence systems built to simulate biological nanomachine constructions in life activities and important biological events in life processes. In this paper, BIM virtual scenarios incorporating intelligent nanomaterial robot mapping technology are migrated to bridge engineering safety management. According to the collected construction scene images and BIM (building information model) virtual scenes, build the construction scene dataset, use intelligent robots to obtain construction scenes, and load them into the trained construction on-the-spot neural network modeling to complete the bridge construction scene identification safety discrimination to select more comprehensive and more stringent management solutions as safety management personnel management methods on-the-spot.

Bin Zhang, Hua Zhao, Chengjun Tan et al.

For bridges with surface foundations, scour is one of the main reasons for bridge failures. In regard to structural health monitoring, vibration-based scour detection techniques have received increasing attention over the past two decades. Scour occurs below the water surface in rivers or sea, leading to difficulty in equipment installation and maintenance. Recently, the concept of “drive-by” SHM using the indirect measurement of passing vehicle responses has been developed rapidly due to its convenience and low cost. This paper proposes a method to detect scour using the vehicle responses under an operational vehicle speed. The wavelet transform was applied to vehicle accelerations to obtain the wavelet energy. It was found that the wavelet energy increases with the increase in the scour damage level. However, the wavelet energy may also be affected by the on-site operating environments, such as sensor noise and other variabilities, which interferes with the identification of scour in practice. Hence, in this work, a statistical-wavelet-based approach was presented to effectively detect the presence of scour and even its location. The feasibility of the proposed approach is verified in both numerical simulation and lab experiments. The results show that the proposed method has a good potential to detect scour using indirect measurements.

ZHU Shuai-run 1, 2, LI Shao-hong 3, HE Bo 1, 2, WU Li-zhou 1

Richards' equation is widely used in the simulation of unsaturated seepage and related fields. In the numerical solution, the finite volume method can be used for numerical discretization, and then the Picard method can be used for iterative solution. However, in order to obtain reliable and accurate numerical solutions, the spatial step size of a uniform grid is usually very small, especially under some unfavorable numerical conditions, such as rainfall infiltration into dry soil, which often makes the iterative process time-consuming or even unable to converge. Therefore, combined with the non-uniform grid in the form of Chebyshev, an improved Picard iteration method (NTG-PI) is proposed based on the non-uniform two-grid correction scheme. Through three examples of unsaturated seepage, the numerical accuracy, convergence rate and acceleration effect of the improved algorithm are validated by comparing the traditional methods and analytical solutions. The results show that compared with the traditional Picard and the adaptive relaxation Picard methods, the proposed method NTG-PI can obtain higher numerical accuracy with a smaller number of discrete nodes, and also has higher computational efficiency. The proposed method can provide a reference for the numerical simulation of unsaturated seepage.

Wang Yi, Wang Jun, Wang Jie et al.

Zhao Wei, Mao Yufei, Zhang Dong

This paper studies the classification of association standards of the general drawings of highway engineering design through investigation and analysis and sets up the system of the association standards. The classification management of general drawing standard system is carried out through research. The ten categories of general drawing management are proposed including general design, highway alignment, subgrade, pavement, bridge and culvert, tunnel, road intersection, traffic engineering and road facilities, environmental protection and landscape design, other engineering, road construction materials.