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Effect of earth reinforcement, soil properties and wall properties on bridge MSE walls

《结构与土木工程前沿（英文）》 2021年 第15卷 第5期   页码 1209-1221 doi: 10.1007/s11709-021-0764-2

摘要： Mechanically stabilized earth (MSE) retaining walls are popular for highway bridge structures. They have precast concrete panels attached to earth reinforcement. The panels are designed to have some lateral movement. However, in some cases, excessive movement and even complete dislocation of the panels have been observed. In this study, 3-D numerical modeling involving an existing MSE wall was undertaken to investigate various wall parameters. The effects of pore pressure, soil cohesion, earth reinforcement type and length, breakage/slippage of reinforcement and concrete strength, were examined. Results showed that the wall movement is affected by soil pore pressure and reinforcement integrity and length, and unaffected by concrete strength. Soil cohesion has a minor effect, while the movement increased by 13–20 mm for flexible geogrid reinforced walls compared with the steel grid walls. The steel grid stresses were below yielding, while the geogrid experienced significant stresses without rupture. Geogrid reinforcement may be used taking account of slippage resistance and wall movement. If steel grid is used, non-cohesive soil is recommended to minimize corrosion. Proper soil drainage is important for control of pore pressure.

关键词： mechanically stabilized earth walls     precast concrete panels     backfill soil     finite element modeling     earth reinforcement    

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Seismic effects on reinforcement load and lateral deformation of geosynthetic-reinforced soil walls

《结构与土木工程前沿（英文）》 2021年 第15卷 第4期   页码 1001-1015 doi: 10.1007/s11709-021-0734-8

摘要： Current design methods for the internal stability of geosynthetic-reinforced soil (GRS) walls postulate seismic forces as inertial forces, leading to pseudo-static analyses based on active earth pressure theory, which yields unconservative reinforcement loads required for seismic stability. Most seismic analyses are limited to the determination of maximum reinforcement strength. This study aimed to calculate the distribution of the reinforcement load and connection strength required for each layer of the seismic GRS wall. Using the top-down procedure involves all of the possible failure surfaces for the seismic analyses of the GRS wall and then obtains the reinforcement load distribution for the limit state. The distributions are used to determine the required connection strength and to approximately assess the facing lateral deformation. For sufficient pullout resistance to be provided by each reinforcement, the maximum required tensile resistance is identical to the results based on the Mononobe–Okabe method. However, short reinforcement results in greater tensile resistances in the mid and lower layers as evinced by compound failure frequently occurring in GRS walls during an earthquake. Parametric studies involving backfill friction angle, reinforcement length, vertical seismic acceleration, and secondary reinforcement are conducted to investigate seismic impacts on the stability and lateral deformation of GRS walls.

关键词： geosynthetics     reinforced soil     retaining walls     seismic performance    

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Shaking table test of composite foundation reinforcement of saturated silty soil for high speed railway

JIANG Guanlu, LIU Xianfeng, ZHANG Jianwen, ZHAO Ruyi

《结构与土木工程前沿（英文）》 2007年 第1卷 第3期   页码 353-360 doi: 10.1007/s11709-007-0047-6

摘要： Three shaking table model tests were conducted with a geometrical scale of 1:10 using a large-scale laminar shear box to investigate the reinforcement effects of compacted gravel column-net composite foundation and cement fly-ash gravel (CFG) column-net composite foundation on the saturated silty soil along the Beijing-Shanghai High Speed Railway. The research results indicate that the increase in excess pore water pressure can be restrained effectively by the compacted gravel column-net composite foundation to improve the anti-liquefaction ability of the ground, and that shear displacement of the ground can be reduced greatly by the compacted gravel column-net and CFG column-net composite foundations to improve the capability of resisting shear displacement of ground. Furthermore, the amplifying of response acceleration, induced by foundation liquefaction, and the settlement of foundation and subgrade can be reduced greatly by the compacted gravel column-net and CFG column-net composite foundations to improve the aseismatic property of the foundation and subgrade.

关键词： large-scale     CFG column-net     amplifying     displacement     subgrade    

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The Rion-Antirion bridge—when a dream becomes reality

Jacques COMBAULT

《结构与土木工程前沿（英文）》 2011年 第5卷 第4期   页码 415-426 doi: 10.1007/s11709-011-0130-x

摘要： Opened to traffic in August 2004, the Rion-Antirion Bridge crosses the Gulf of Corinth near Patras in western Greece. It consists of an impressive multi cable-stayed span bridge connected to the land by two approaches. An exceptional combination of physical conditions made this project quite unusual: high water depth, deep strata of weak soil, strong seismic activity and fault displacements. In addition a risk of heavy ship collision had to be taken into account. The structure has been designed in view of challenging severe earthquakes and ensuring the everyday serviceability of the link as well. To make the bridge feasible, innovative techniques had to be developed: The strength of the in situ soil has been improved by means of inclusions; the bridge deck has been suspended on its full length, and therefore isolated as much as it can be.

关键词： bridge     multi cable-stayed spans     soil reinforcement     inclusions     yield theory     capacity design     push-over     dry dock     wet dock    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Punching of reinforced concrete slab without shear reinforcement: Standard models and new proposal

Luisa PANI, Flavio STOCHINO

《结构与土木工程前沿（英文）》 2020年 第14卷 第5期   页码 1196-1214 doi: 10.1007/s11709-020-0662-z

摘要： Reinforced concrete (RC) slabs are characterized by reduced construction time, versatility, and easier space partitioning. Their structural behavior is not straightforward and, specifically, punching shear strength is a current research topic. In this study an experimental database of 113 RC slabs without shear reinforcement under punching loads was compiled using data available in the literature. A sensitivity analysis of the parameters involved in the punching shear strength assessment was conducted, which highlighted the importance of the flexural reinforcement that are not typically considered for punching shear strength. After a discussion of the current international standards, a new proposed model for punching shear strength and rotation of RC slabs without shear reinforcement was discussed. It was based on a simplified load-rotation curve and new failure criteria that takes into account the flexural reinforcement effects. This experimental database was used to validate the approaches of the current international standards as well as the new proposed model. The latter proved to be a potentially useful design tool.

关键词： punching shear strength     reinforced concrete     slabs     reinforcement ratio    

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浅埋大跨公路隧道穿越富水软弱土层施工关键技术

赵立财

《中国工程科学》 2015年 第17卷 第1期   页码 81-87

摘要：

以厦漳公路（厦门段）东孚隧道施工为例，介绍了?229大管棚跟管钻进咬合相接加固技术+十字交叉隔壁后拆式开挖技术在浅埋富水软弱土层隧道开挖过程中的应用；同时利用TGMIS动态智能化监测系统对施工沉降状况进行了数据采集、分析及处理，使围岩变形得到控制，顺利通过了浅埋软弱土层段掘进施工。

关键词： 浅埋；富水软弱土；隧道；管棚；加固；十字交叉隔壁后拆式；开挖    

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Spatial embedded reinforcement of 20-node block element for analysis PC bridges

LONG Peiheng, DU Xianting, CHEN Weizhen

《结构与土木工程前沿（英文）》 2008年 第2卷 第3期   页码 274-280 doi: 10.1007/s11709-008-0039-1

摘要： The formula for the contribution of prestressed reinforcement on embedded reinforcement element is derived according to the mechanical behavior of PC bridges and the foundational principle of finite element method. Mechanical concept is definite and examples validate the calculation results. Reinforcement element model allows generating a finite element mesh without taking into consideration the layout of reinforcements. Furthermore, the prestressing tendon may pass through the concrete elements in an arbitrary manner. It is an effective approach that the no-node loads are diverted from the tendons to the adjacent concrete elements. A useful arithmetic analysis of the spatial curved tendon PC Bridges is provided.

关键词： arithmetic analysis     calculation     prestressed reinforcement     mechanical     arbitrary    

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Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

《工程（英文）》 doi: 10.1016/j.eng.2023.07.018

摘要： The forward design of trajectory planning strategies requires preset trajectory optimization functions, resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits. In addition, owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios, it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters. Therefore, an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed. First, numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset. Subsequently, a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory. Furthermore, a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function, and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed. Finally, the proposed strategy is verified based on real driving scenarios. The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the “emergency degree” of obstacle avoidance and the state of the vehicle. Moreover, this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories, effectively improving the adaptability and acceptability of trajectories in driving scenarios.

关键词： Obstacle avoidance trajectory planning     Inverse reinforcement theory     Anthropomorphic     Adaptive driving scenarios    

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Automated synthesis of steady-state continuous processes using reinforcement learning

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 288-302 doi: 10.1007/s11705-021-2055-9

摘要： Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

关键词： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcement learning    

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

《结构与土木工程前沿（英文）》 2022年 第16卷 第11期   页码 1397-1414 doi: 10.1007/s11709-022-0860-y

摘要： This paper proposes a framework for critical element identification and demolition planning of frame structures. Innovative quantitative indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learning and graph embedding. The action is defined as removing an element, and the state is described by integrating the joint and element features into a comprehensive feature vector for each element. By establishing the policy network, the agent outputs the Q value for each action after observing the state. Through numerical examples, it is confirmed that the trained agent can provide an accurate estimation of the Q values, and handle problems with different action spaces owing to utilization of graph embedding. Besides, different behaviors can be learned by varying hyperparameters in the reward function. By comparing the proposed method and the conventional sensitivity index-based methods, it is demonstrated that the computational cost is considerably reduced because the reinforcement learning model is trained offline. Besides, it is proved that the Q values produced by the reinforcement learning agent can make up for the deficiencies of existing indices, and can be directly used as the quantitative index for the decision-making for determining the most expected collapse scenario, i.e., the sequence of element removals.

关键词： progressive collapse     alternate load path     demolition planning     reinforcement learning     graph embedding    

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Layout optimization of steel reinforcement in concrete structure using a truss-continuum model

《结构与土木工程前沿（英文）》 2023年 第17卷 第5期   页码 669-685 doi: 10.1007/s11709-023-0963-0

摘要： Owing to advancement in advanced manufacturing technology, the reinforcement design of concrete structures has become an important topic in structural engineering. Based on bi-directional evolutionary structural optimization (BESO), a new approach is developed in this study to optimize the reinforcement layout in steel-reinforced concrete (SRC) structures. This approach combines a minimum compliance objective function with a hybrid truss-continuum model. Furthermore, a modified bi-directional evolutionary structural optimization (M-BESO) method is proposed to control the level of tensile stress in concrete. To fully utilize the tensile strength of steel and the compressive strength of concrete, the optimization sensitivity of steel in a concrete–steel composite is integrated with the average normal stress of a neighboring concrete. To demonstrate the effectiveness of the proposed procedures, reinforcement layout optimizations of a simply supported beam, a corbel, and a wall with a window are conducted. Clear steel trajectories of SRC structures can be obtained using both methods. The area of ​​critical tensile stress in concrete yielded by the M-BESO is more than 40% lower than that yielded by the uniform design and BESO. Hence, the M-BESO facilitates a fully digital workflow that can be extremely effective for improving the design of steel reinforcements in concrete structures.

关键词： bi-directional evolutionary structural optimization     steel-reinforced concrete     concrete stress     reinforcement method     hybrid model    

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Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

《工程（英文）》 doi: 10.1016/j.eng.2023.10.005

摘要： While autonomous vehicles are vital components of intelligent transportation systems, ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving. Therefore, we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles. The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety. Specifically, an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics. In addition, an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics. Moreover, we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety model. Finally, the proposed approach is evaluated through both simulations and experiments. These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

关键词： Autonomous vehicle     Decision-making     Reinforcement learning     Adversarial attack     Safety guarantee    

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概念加固思想及工程应用

段敬民,钱永久,张方,曾宪桃

《中国工程科学》 2005年 第7卷 第8期   页码 22-25

摘要：

提出了工程结构加固中新的思路——概念加固。概念加固是运用人的思维和判断，从宏观上决定工程加固中的基本问题、基本概念及基本原则，并阐述了基于“概念加固”思想指导下的整体预应力加固技术，介绍了在工程加固中的应用实例。

关键词： 工程结构     概念加固     整体预应力加固技术    

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Monitoring corrosion of reinforcement in concrete structures via fiber Bragg grating sensors

Zhupeng ZHENG, Xiaoning SUN, Ying LEI

《机械工程前沿（英文）》 2009年 第4卷 第3期   页码 316-319 doi: 10.1007/s11465-009-0040-y

摘要： Corrosion of steel and rebar in concrete structures is one of the most frequent reasons for civil infrastructure failures. Thus, improving the effective corrosion sensor technology can greatly reduce cost and provide safe structures with long service lives. However, assessing the corrosion condition of rebars is not simple because they are buried in concrete. In this paper, using fiber Bragg grating (FBG), a corrosion sensor for monitoring steel rebars embedded in a concrete structure is developed and validated by experiments. Based on the fact that the volume and diameter of a rebar embedded in concrete will enlarge due to corrosion, an FBG packaged with fiber-reinforced plastics (FRP) is wrapped on the steel bar. During corrosion, the increase in the bar diameter leads to the increase in fiber strain, which can be measured by the shift of the wavelength of FBG. Performances of the corrosion sensor are validated by accelerating corrosion in lab experiments. The corrosion sensor is embedded in a concrete specimen put in a 5% sodium chloride solution with a constant current. Experimental results show that the corrosion sensor can monitor the concurrence of corrosion of rebars in concrete. The corrosion extent can be quantitatively evaluated through the change in the wavelength of FBG. Therefore, the corrosion sensor developed in this paper is feasible for monitoring the early corrosion of rebars in concrete.

关键词： fiber Bragg grating (FBG)     corrosion     concrete structures     accelerated corrosion test    

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标题 作者 时间 类型 操作

Effect of earth reinforcement, soil properties and wall properties on bridge MSE walls

期刊论文

Seismic effects on reinforcement load and lateral deformation of geosynthetic-reinforced soil walls

期刊论文

Shaking table test of composite foundation reinforcement of saturated silty soil for high speed railway

JIANG Guanlu, LIU Xianfeng, ZHANG Jianwen, ZHAO Ruyi

期刊论文

The Rion-Antirion bridge—when a dream becomes reality

Jacques COMBAULT

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Punching of reinforced concrete slab without shear reinforcement: Standard models and new proposal

Luisa PANI, Flavio STOCHINO

期刊论文

浅埋大跨公路隧道穿越富水软弱土层施工关键技术

赵立财

期刊论文

Spatial embedded reinforcement of 20-node block element for analysis PC bridges

LONG Peiheng, DU Xianting, CHEN Weizhen

期刊论文

Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement

Jian Wu,Yang Yan,Yulong Liu,Yahui Liu,

期刊论文

Automated synthesis of steady-state continuous processes using reinforcement learning

期刊论文

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

期刊论文

Layout optimization of steel reinforcement in concrete structure using a truss-continuum model

期刊论文

Toward Trustworthy Decision-Making for Autonomous Vehicles: A Robust Reinforcement Learning Approach

Xiangkun He,Wenhui Huang,Chen Lv,

期刊论文

概念加固思想及工程应用

段敬民,钱永久,张方,曾宪桃

期刊论文

Monitoring corrosion of reinforcement in concrete structures via fiber Bragg grating sensors

Zhupeng ZHENG, Xiaoning SUN, Ying LEI

期刊论文