Generalized bridge network performance analysis with correlation and time-variant reliability

A novel approach and framework for the analysis of bridge networks is presented. The goal of the analysis is to assess the life-cycle performance of the network and its time-variant reliability. The proposed approach combines three important features that determine its ability to estimate with accuracy and robustness the reliability of a network along its life-cycle.The first one is that the reliability of the individual bridges is modeled as time-variant, due to the deterioration of their structural components. The network performance analysis is repeated for several time instants, with the relative reliability of the bridges. Therefore, the network performance indicators are also time-variant. In this way, the proposed procedure can be used as a basic tool for maintenance planning at the network level.The second asset is that the proposed framework takes into account complex (i.e. “generalized”) network layouts, not necessarily describable using series, parallel or series-parallel models. In fact, when all the possible traffic flows in a network and all the possible trip origins and destinations are considered, it is not feasible, in general, to model the network with a simple scheme. In the present paper, techniques derived from transportation engineering for the traffic flow distribution and assignment are used.The third feature is that the proposed approach considers a correlation structure among the states (in/out of service) of the various bridges of the network. In fact, bridges associated with the same network are likely to share similar characteristics and external loads. Therefore, a correlation structure for the service state of individual bridges is estimated and implemented in the analysis.A case study involving a transportation network with fourteen bridges is presented as a numerical application.     

Bridge retrofit prioritisation for ageing transportation networks subject to seismic hazards

The deteriorating state of highway bridges is traditionally ignored in estimating the seismic reliability of transportation networks. In this study, the present day seismic reliability of ageing bridges in highway networks is evaluated through a time-dependent seismic fragility analysis of typical bridge classes. An efficient algorithm based on finite-state Markov Chain Monte Carlo simulations is also presented to assess the reliability of large ageing highway bridge networks without the need to simplify the network topology. The criticality of ageing bridges is then assessed through different proposed ranking strategies to arrive at an optimised seismic retrofit prioritisation. A case study on an existing bridge network with 515 bridges in the state of South Carolina, USA reveals striking differences between results of the proposed ranking strategies and those from state-of-the-practice methods. Such differences emphasise the significance of accounting for network-level importance in seismic retrofit programs of ageing transportation networks.     

Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization

Many bridge structures, one of the most critical components in transportation infrastructure systems, exhibit signs of deteriorations and are approaching or beyond the initial design service life. Therefore, structural health inspections of these bridges are becoming critically important, especially after extreme events. To enhance the efficiency of such an inspection, in recent years, autonomous damage detection based on computer vision has become a research hotspot. This article proposes a three‐level image‐based approach for post‐disaster inspection of the reinforced concrete bridge using deep learning with novel training strategies. The convolutional neural network for image classification, object detection, and semantic segmentation are, respectively, proposed to conduct system‐level failure classification, component‐level bridge column detection, and local damage‐level damage localization. To enable efficient training and prediction using a small data set, the model robustness is a crucial aspect to be taken into account, generally through its hyperparameters’ selection. This article, based on Bayesian optimization, proposes a principled manner of such selection, with which very promising results (well over 90% accuracies) and robustness are observed on all three‐level deep learning models.     

Rethinking bus-to-metro accessibility in new town development: Case studies in Shanghai

With the rapid urbanization in cities around the world, new towns that are close to the existing urban fringe have been developed to accommodate the increasing population. However, due to the long development time of the infrastructure systems in these new towns, the establishment of public transportation services usually lags behind the population expansion. Therefore, to ensure urban accessibility, governments utilize bus networks to bridge the connectivity gaps of metro systems. To assist the design and decision-making required for bus and metro interconnectivity and optimize public transportation networks, this study proposed a quantitative network-based framework. The proposed framework extended the existing social network analysis theory and identified five indicators to assess and optimize the network design. To validate the proposed method, nine typical cases in Shanghai were examined. The results based on the proposed analysis framework suggest that more edges between access points (bus stops within the walkable area of a metro station entrance) and other nodes can improve the accessibility of the study area and create a well-integrated system. Therefore, this study is able to provide an insightful understanding of intermodal transportation coordination and transport facility arrangement.     

基于并行NSGA-Ⅱ算法的桥梁网络养护策略优化

制定辖区全体桥梁的养护策略是在役桥梁管养方法研究的前进方向。给出桥梁网络养护策略的优化目标与约束方程,建立网级桥梁策略优化框架。引入带精英策略的非支配排序遗传算法(NSGA-Ⅱ),将其与并行遗传算法(PGA)相结合,提出基于并行NSGA-Ⅱ的网级养护策略优化方法。采用约束联赛法处理单座桥梁的最低性能约束问题,通过修改支配关系的定义改进原有NSGA-Ⅱ算法。应用于工程案例,输出结果为一系列Pareto最优解集,管理方可根据自身实际方便选择。算例分析验证该框架的有效性,以及建议算法的可靠性与高效性。     

Resilience-based post-disaster recovery strategies for road-bridge networks

This paper presents a novel resilience-based framework to optimise the scheduling of the post-disaster recovery actions for road-bridge transportation networks. The methodology systematically incorporates network topology, redundancy, traffic flow, damage level and available resources into the stochastic processes of network post-hazard recovery strategy optimisation. Two metrics are proposed for measuring rapidity and efficiency of the network recovery: total recovery time (TRT) and the skew of the recovery trajectory (SRT). The TRT is the time required for the network to be restored to its pre-hazard functionality level, while the SRT is a metric defined for the first time in this study to capture the characteristics of the recovery trajectory that relates to the efficiency of those restoration strategies considered. Based on this two-dimensional metric, a restoration scheduling method is proposed for optimal post-disaster recovery planning for bridge-road transportation networks. To illustrate the proposed methodology, a genetic algorithm is used to solve the restoration schedule optimisation problem for a hypothetical bridge network with 30 nodes and 37 bridges subjected to a scenario seismic event. A sensitivity study using this network illustrates the impact of the resourcefulness of a community and its time-dependent commitment of resources on the network recovery time and trajectory.     

Life-cycle reliability assessment of reinforced concrete bridges under multiple hazards

This paper proposes a novel probabilistic methodology for estimating the life-cycle reliability of existing reinforced concrete (RC) bridges under multiple hazards. The life-cycle reliability of an RC bridge pier under seismic and airborne chloride hazards is compared to that of a bridge girder under traffic and airborne chloride hazards. When conducting a life-cycle reliability assessment of existing RC bridges, observational data from inspections can provide the corrosion level in reinforcement steel. Random variables related with the prediction of time-variant steel weight loss can be updated based on the inspection results using Sequential Monte Carlo Simulation (SMCS). This paper presents a novel procedure for identifying the hazards that most threaten the structural safety of existing RC bridges, as well as the structural components with the lowest reliability when these bridges are exposed to multiple hazards. The proposed approach, using inspection results associated with steel weight loss, provides a rational reliability assessment framework that allows comparison between the life-cycle reliabilities of bridge components under multiple hazards, helping the prioritisation of maintenance actions. The effect of the number of inspection locations on the updated reliability is considered by incorporating the spatial steel corrosion distribution. An illustrative example is provided of applying the proposed life-cyle reliability assessment to a hypothetical RC bridge under multiple hazards.     

Bridge performance indicators based on traffic load monitoring

The assessment of the existing road bridges as a part of bridge management networks has been a subject of several European research projects and actions in the last few decades. Bridge management includes a series of activities, interconnected in order to achieve optimal balancing of required costs, potential risks and overall bridge performance. This paper discusses a valuable link between a specific indicator (traffic load information calculated using Bridge Weigh-in-Motion measurement data) and the corresponding structural performance (reliability of existing bridge). It is shown that implementation of weigh-in-motion measurements in the probabilistic assessment of existing road bridges can reveal hidden bridge reserves and predict bridge reliability development over the required lifetime. Consequently, such approach can yield an unrestricted use of the bridge over a much longer remaining service life. Broader advantages lie in an improved road network management, road bridges in particular, and in a more sustainable development of infrastructure network and greater satisfaction of road users and owners.     

Integration of structural health monitoring in a system performance based life-cycle bridge management framework

In this paper, an approach for integrating the information obtained from structural health monitoring in a life-cycle bridge management framework is proposed. The framework is developed on the basis of life-cycle system performance concepts that are also presented in this paper. The performance of the bridge is quantified by incorporating prior knowledge and information obtained from structural health monitoring using Bayesian updating concepts. This performance is predicted in the future using extreme value statistics. Advanced modelling tools and techniques are used for the lifetime reliability computations, including incremental nonlinear finite element analyses, quadratic response surface modelling using design of experiments concepts, and Latin hypercube sampling, among other techniques. The methodology is illustrated on an existing bridge in the state of Wisconsin.     

Frontier of continuous structural health monitoring system for short & medium span bridges and condition assessment

It is becoming an important social problem to make maintenance and rehabilitation of existing short and medium span(10-20 m) bridges because there are a huge amount of short and medium span bridges in service in the world. The kernel of such bridge management is to develop a method of safety(condition) assessment on items which include remaining life and load carrying capacity. Bridge health monitoring using information technology and sensors is capable of providing more accurate knowledge of bridge performance than traditional strategies. The aim of this paper is to introduce a state-of-the-art on not only a rational bridge health monitoring system incorporating with the information and communication technologies for lifetime management of existing short and medium span bridges but also a continuous data collecting system designed for bridge health monitoring of mainly short and medium span bridges. In this paper, although there are some useful monitoring methods for short and medium span bridges based on the qualitative or quantitative information, mainly two advanced structural health monitoring systems are described to review and analyse the potential of utilizing the long term health monitoring in safety assessment and management issues for short and medium span bridge. The first is a special designed mobile in-situ loading device(vehicle) for short and medium span road bridges to assess the structural safety(performance) and derive optimal strategies for maintenance using reliability based method. The second is a long term health monitoring method by using the public buses as part of a public transit system (called bus monitoring system) to be applied mainly to short and medium span bridges, along with safety indices, namely, “characteristic deflection” which is relatively free from the influence of dynamic disturbances due to such factors as the roughness of the road surface, and a structural anomaly parameter.     

Bridge life-cycle performance and cost: analysis,prediction, optimisation and decision-making

The development of a generalised framework for assessing bridge life-cycle performance and cost, with emphasis on analysis, prediction, optimisation and decision-making under uncertainty, is briefly addressed. The central issue underlying the importance of the life-cycle approach to bridge engineering is the need for a rational basis for making informed decisions regarding design, construction, inspection, monitoring, maintenance, repair, rehabilitation, replacement and management of bridges under uncertainty which is carried out by using multi-objective optimisation procedures that balance conflicting criteria such as performance and cost. A number of significant developments are summarised, including time-variant reliability, risk, resilience, and sustainability of bridges, bridge transportation networks and interdependent infrastructure systems. Furthermore, the effects of climate change on the probabilistic life-cycle performance assessment of highway bridges are addressed. Moreover, integration of SHM and updating in bridge management and probabilistic life-cycle optimisation considering multi-attribute utility and risk attitudes are presented.     

Reliability assessment of cable-stayed bridges based on structural health monitoring techniques

This paper presents the reliability analysis approach of long-span cable-stayed bridges based on structural health monitoring (SHM) technology. First, the framework of structural reliability analysis is recognised based on SHM. The modelling approach of vehicle loads and environmental actions and the extreme value of responses based on SHM are proposed, and then models of vehicle and environmental actions and the extreme value of inner force are statistically obtained using the monitored data of a cable-stayed bridge. For the components without FBG strain sensors, the effects and models (extreme values) of dead load, unit temperature load, and wind load of the bridge can be calculated by the updated finite element model and monitored load models. The bearing capacity of a deteriorated structure can be obtained by the updated finite element model or durability analysis. The reliability index of the bridge's critical components (stiffening girder in this study) can be estimated by using a reliability analysis method, e.g. first order reliability method (FORM) based on the models of extreme value of response and ultimate capacity of the structure. Finally, the proposed approach is validated by a practical long-span cable-stayed bridge with the SHM system. In the example, reliability indices of the bridge's stiffening girder at the stage after repair and replacement after 18 years of operation, and the damaged stage are evaluated.     

A two‐level mixed‐integer programming model for bridge replacement prioritization

A bridge network is an essential part of the transportation system. Therefore, the restoration and replacement activities of aging bridges result in severe traffic delays and disruptions that heavily impact the daily traffic. Accelerated bridge construction (ABC) techniques are rapidly gaining acceptance as an alternative to conventional construction due to reduced construction duration and minimum closure impact at the network level. The limitations and completion rates vary depending on types of ABC. There is a trade‐off between a faster ABC technique with higher investment and a faster construction of a critical bridge in the network resulting large savings to users. To provide a balanced portfolio of ABC techniques on bridge sites and the prioritization of bridges for replacement, this paper develops a mixed‐integer programming (MIP) model with two levels. In this model, a network‐level scheme is used to select bridges for rapid replacement based on their criticality to the network, and a project‐level scheme is used to optimize the choice of ABC techniques for each selected bridge. To account for the effects of different construction strategies for bridge replacement, the costs associated with each replacement activity are calculated, including direct costs from the actual replacement of bridges and indirect costs experienced by network users due to bridge closures during maintenance. Using the MIP model and based on investment, outcomes are estimated for the enhanced serviceability, efficient ABC techniques, an optimal bridge replacement strategy, and minimized total cost during the entire process. These outcomes could provide decision makers and stakeholders with a complete understanding of the prioritization process at both the network and project levels.     

Integrated condition rating and forecasting method for bridge decks using Visual Inspection and Ground Penetrating Radar

The growing problem of bridge deterioration globally has imposed prominent challenges on transportation agencies, mainly in terms of ensuring safety and serviceability of the bridge infrastructure. The large number of bridges built during the 20th century has aged and produced a complex decision-making problem that departments of transportation need to deal with. Bridge management, as a particular domain of infrastructure asset management, has focused on developing methods for condition rating and deterioration modeling. The current research reviews bridge inspection practices and identifies the main defects and deterioration signs of concrete bridge decks that are typically captured by Visual Inspection (VI) and Non-Destructive Evaluation (NDE) techniques. The research introduces the Quality Function Deployment (QFD) theory and Weibull Distribution Function (WDF) as an integrated novel method to the area of bridge condition assessment and deterioration modeling. The proposed QFD condition assessment model is developed based on integrating VI and Ground Penetrating Radar (GPR) evaluation results to provide consistent condition ratings and performance predictions. The QFD model is demonstrated with a real case study and compared to other condition assessment models. Moreover, the QFD method is validated with data extracted from twenty bridge inspection reports completed by bridge inspectors and assessed by bridge experts. The developed deterioration curves using the reliability function for the Weibull distribution show absolute matching in these results through predicting the structure future performance and defining its useful service life. Accordingly, these models can enhance bridge Maintenance, Repair and Replacement (MRR) decisions since they produce reliable condition ratings and predictions that can link to proper rehabilitation action, and eventually assist in the decision making and planning for the selected MRR action. All these processes are integrated within one framework.     

Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

To optimize mitigation, preparedness, response, and recovery procedures for infrastructure systems, it is essential to use accurate and efficient means to evaluate system reliability against probabilistic events. The predominant approach to quantify the impact of natural disasters on infrastructure systems is the Monte Carlo approach, which still suffers from high computational cost, especially when applied to large systems. This article presents a deep learning framework for accelerating seismic reliability analysis, on a transportation network case study. Two distinct deep neural network surrogates are constructed and studied: (1) a classifier surrogate that speeds up the connectivity determination of networks and (2) an end‐to‐end surrogate that replaces modules such as roadway status realization, connectivity determination, and connectivity averaging. Numerical results from k‐terminal connectivity analysis of a California transportation network subject to a probabilistic earthquake event demonstrate the effectiveness of the proposed surrogates in accelerating reliability analysis while achieving accuracies of at least 99%.     

Maintenance prioritization method for networked bridges

Bridge owners and their representatives currently face ever-increasing tasks to effectively maintain normal functionality of a huge inventory of deteriorated bridges. This is due to adverse impacts of local defects, traffic loading and vibrations, disasters, aggressive environments, etc. The allocated budget is usually small and covers only 30% to 70% of the actual maintenance needs, and so the necessary site maintenance is compulsorily postponed or cancelled. This fact gives a difficult problem to be solved: whether the overall performance of all bridges should be ensured, or whether maximum values of maintenance resources should be generated. This article therefore presents a computational procedure called ‘the maintenance prioritization method’ to prioritize maintenance work in accordance with available allocated resources, based on a computerized database. For each fiscal year, the priority indices are calculated for all bridges to determine the prioritized work and to select the scope of site maintenance. The priority index is calculated by considering several indicators such as the bridge speciality, the health condition, or the maintenance benefit (which is calculated individually or by whole origin-destination network analysis). The proposed method has been practically applied to a specific bridge network in Vietnam to examine whether it is useful to prioritize the most necessary maintenance work such as deteriorated bridges, whose performance is crucial to the transportation network. Currently, the application of this method is limited to a local network in one fiscal year, but in the future it could be extended to a whole country and used to establish long-term strategies.     

土坎乌江大桥准直点激光投射式挠度监测系统

桥梁是交通基础设施中的重要组成部分,而桥梁挠度则是反映桥梁结构性能最为重要的技术参数之一。从国内外同类研究、同类技术中可实现桥梁结构挠度长期健康监测的方法中选择激光图像法,对其现有技术上所遇到的开发性价比合适的高质量点光源激光器和如何适当扩大光接受芯片标靶的测量范围两方面的难题进行了突破。研发了准直点激光投射式挠度监测方法,该方法是精确测量特别是大跨径桥梁挠度变化的一种有效方法。本文设计并成功安装了土坎乌江大桥准直点激光投射式挠度监测系统,并对该桥近期(2010.1～2010.6)监测数据进行分析,结果显示结构最大挠度在2cm以内,桥梁运行状况良好。该系统从08年安装至今一直运行正常,系统稳定性较好,实现了桥梁结构挠度的在线监测。     

市域综合交通规划的特点与技术思路探讨——以杭州为例

市域综合交通规划与城市综合交通规划有很大的差别,不能完全套用传统的城市交通规划方法。在对市域综合交通规划的特点和难点进行剖析的基础上,杭州市确立了以交通发展促进城乡统筹、区域统筹和城市空间拓展的规划目标,以市域整体发展为依据,完善和提升重大交通设旋功能,构建市域综合交通框架,利用综合交通枢纽实现交通体化和网络结构优化。     

A probabilistic deep reinforcement learning approach for optimal monitoring of a building adjacent to deep excavation

During a deep excavation project, monitoring the structural health of the adjacent buildings is crucial to ensure safety. Therefore, this study proposes a novel probabilistic deep reinforcement learning (PDRL) framework to optimize the monitoring plan to minimize the cost and excavation-induced risk. First, a Bayesian-bi-directional general regression neural network is built as a probabilistic model to describe the relationship between the ground settlement of the foundation pit and the safety state of the adjacent building, along with the actions in a dynamic manner. Subsequently, a double deep Q-network method, which can capture the realistic features of the excavation management problem, is trained to form a closed decision loop for continuous learning of monitoring strategies. Finally, the proposed PDRL approach is applied to a real-world deep excavation case in No. 14 Shanghai Metro. This approach can estimate the time-variant probability of damage occurrence and maintenance actions and update the state of the adjacent building. According to the strategy proposed via PDRL, monitoring of the adjacent buildings begins in the middle stage rather than on the first day of the excavation project if there is full confidence in the quality of the monitoring data. When the uncertainty level of data rises, the starting day might shift to an earlier date. It is worth noting that the proposed PDRL method is adequately robust to address the uncertainties embedded in the environment and model, thus contributing to optimizing the monitoring plan for achieving cost-effectiveness and risk mitigation.     

Long-term performance assessment of the Telegraph Road Bridge using a permanent wireless monitoring system and automated statistical process control analytics

The purpose of this study is to advance wireless sensing technology for permanent installation in operational highway bridges for long-term automated health assessment. The work advances the design of a solar-powered wireless sensor network architecture that can be permanently deployed in harsh winter climates where limited solar energy and cold temperatures are normal operational conditions. To demonstrate the performance of the solar-powered wireless sensor network, it is installed on the multi-steel girder bridge carrying northbound I-275 traffic over Telegraph Road (Monroe, Michigan) in 2011; a unique design feature of the bridge is the use of pin and hanger connections to support the bridge main span. A dense network of strain gauges, accelerometers and thermometers are installed to acquire bridge responses of interest to the bridge manager including responses that would be affected by long-term bridge deterioration. The wireless monitoring system collects sensor data on a daily schedule and communicates the data to the Internet where it is stored in a curated data repository. Bridge response data in the repository are autonomously processed to extract truck load events using machine learning, compensate for environmental variations using nonlinear regression and to quantitatively assess anomalous bridge performance using statistical process control.