Explore the vast range of titles available.

This article discusses developing a methodology based on visual inspection for quantifying bridge damage (WABIM). The proposed methodology was developed through the application of weighted averages and a case study. Many current visual inspection methodologies, manuals, or guides related to bridges only allow qualitative results to be determined. Consequently, a high degree of inefficiency and inaccuracy was identified in the results from traditional methodologies; since they have a subjective approach, the results merely depend on the observer. Therefore, a methodological proposal was generated that allowed qualitative results to be described quantitatively, increasing the objectivity of the analysis and the accuracy of bridge maintenance plans. Rating ranges are used with weighted averages for each pathology, applied directly to the structural elements of the bridges. The classification guidelines and pathologies of bridge structures are adapted according to the Manual for the Visual Inspection of Bridges and Pontoons of Invías, Colombia. The case study was developed on a bridge in the city of Pereira, Colombia, presenting more significant surface deterioration and equipment deterioration. The WABIM methodology identified that periodic maintenance is required and the intervention's emphasis.

In this paper, the stress analysis of the most critical beam–arch joint of Yuehu Bridge is conducted, despite the variation in the specific structure of each tied arch bridge. To achieve this, two specimens with different scale ratios were designed. The smaller specimen was used to consider the effect of bridge deck and loading to failure. The experimental results indicate that both specimens did not exhibit significant deformation under the design load, and the measuring point’s stress was located in the elastic section. This implies that the original bridge structure design is rational. However, the arch rib steel plate of the 1/8 scale specimen buckled when subjected to 1.8 times the design load. To validate the experimental results, a finite element model that considers the elastoplastic behavior of the material was established and compared with the experimental results. The comparison shows that the finite element model can predict the mechanical behavior of the structure effectively, thus confirming the rationality of the structure design. Additionally, the study also analyzed the buckling problem of tied arch bridges, which is another critical issue. The in-plane and out-of-plane buckling of fixed and hinged parabolic arches under uniform axial compression were investigated. The results demonstrate that the boundary conditions, rise-span ratio, and bridge deck width significantly affect the buckling performance. Overall, this study provides essential insights into the stress and buckling behavior of tied arch bridges, which can guide the design and construction of such structures in the future.

Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Maintenance and rehabilitation needs of bridge infrastructure are periodically monitored and assessed, typically every two years. Existing inspection techniques, such as visual inspection, are time-consuming, subjective, and often incomplete. Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. The primary objective of this study is to conduct a comprehensive review of the application of UAVs in bridge condition monitoring, used in conjunction with remote sensing technologies. Remote sensing technologies such as visual imagery, infrared thermography, LiDAR, and other sensors, integrated with UAVs for data acquisition are analyzed in depth. This study compiled sixty-five journal and conference papers published in the last two decades scrutinizing NDT-based UAV systems. In addition to comparison of stand-alone and integrated NDT-UAV methods, the facilitation of bridge inspection using UAVs is thoroughly discussed in the present article in terms of ease of use, accuracy, cost-efficiency, employed data collection tools, and simulation platforms. Additionally, challenges and future perspectives of the reviewed UAV-NDT technologies are highlighted.

A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness.

The synchronous construction of the pylon and cables of a cable-stayed bridge without backstays has the characteristics of a short construction period and reduced support costs. However, it also increases the difficulty of construction control, making the reasonable completion state of the bridge more complex. To investigate the impact of various load parameters on the structural state of a cable-stayed bridge without backstays during the synchronous construction process, and to ensure a rational final bridge state, this study proposes an assessment framework for evaluating the internal forces of the bridge. The framework initially uses the response surface method to establish explicit equations relating the control indicators of the bridge’s final state to various load parameters. Subsequently, through sensitivity analysis, the degree of influence of each load parameter on the structural response of the cable-stayed bridge without backstays is examined. The most sensitive factors are identified to create a bridge parameter influence library, which helps reduce computational costs. Based on this, a method for controlling construction errors and predicting cable forces is proposed. This method utilizes the pre-established bridge parameter influence library, combined with the internal force state of the bridge at the current construction stage, to accurately predict the tension force of the stay cables in the subsequent stage, thereby ensuring a rational final bridge state. The framework is ultimately validated through a case study of the Longgun River Bridge to assess its rationality and effectiveness.

The deterioration of a bridge’s deck endangers its safety and serviceability. Ohio has approximately 45,000 bridges that need to be monitored to ensure their structural integrity. Adequate prediction of the deterioration of bridges at an early stage is critical to preventing failures. The objective of this research was to develop an accurate model for predicting bridge deck conditions in Ohio. A comprehensive literature review has revealed that past researchers have utilized different algorithms and features when developing models for predicting bridge deck deterioration. Since, there is no guarantee that the use of features and algorithms utilized by past researchers would lead to accurate results for Ohio’s bridges, this research proposes a framework for optimizing the use of machine learning (ML) algorithms to more accurately predict bridge deck deterioration. The framework aims to first determine “optimal” features that can be related to deck deterioration conditions, specifically in the case of Ohio’s bridges by using various feature-selection methods. Two feature-selection models used were XGboost and random forest, which have been confirmed by the Boruta algorithm, in order to determine the features most relevant to deck conditions. Different ML algorithms were then used, based on the “optimal” features, to select the most accurate algorithm. Seven machine learning algorithms, including single models such as decision tree (DT), artificial neural networks (ANNs), k-nearest neighbors (k-NNs), logistic regression (LR), and support vector machines (SVRs), as well as ensemble models such as Random Forest (RF) and eXtreme gradient boosting (XGboost), have been implemented to classify deck conditions. To validate the framework, results from the ML algorithms that used the “optimal” features as input were compared to results from the same ML algorithms that used the “most common” features that have been used in previous studies. On a dataset obtained from the Ohio Department of Transportation (ODOT), the results indicated that the ensemble ML algorithms were able to predict deck conditions significantly more accurately than single models when the “optimal” features were utilized. Although the framework was implemented using data obtained from ODOT, it can be successfully utilized by other transportation agencies to more accurately predict the deterioration of bridge components.

Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based computer vision technique with the influence line theory. Theoretical background and derivations are introduced from both aspects of structural analysis and computer vision techniques. In addition, to evaluate the effectiveness and accuracy of the proposed traffic sensing method through field tests, a systematic analysis is performed on a continuous box-girder bridge. The obtained results show that the proposed method can automatically identify the vehicle load and speed with promising efficiency and accuracy and most importantly cost-effectiveness. All these features make the proposed methodology a desirable bridge weigh-in-motion system, especially for bridges already equipped with structural health monitoring system.

In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model.

Defects are essential indicators to gauge the structural integrity and safety of reinforced concrete bridges. Non-destructive inspection has been pervasively explored over the last three decades to localize and characterize surface and subsurface anomalies in reinforced concrete bridges. In addition, different fuzzy set theory-based, computer vision and artificial intelligence algorithms were leveraged to analyze the data garnered from non-destructive evaluation techniques. In light of the foregoing, this research paper presents a mixed review method that encompasses both bibliometric and systematic analyses of the state-of-the-art work pertinent to the assessment of reinforced concrete bridge defects using non-destructive techniques (CBD_NDT). In this context, this study reviews the literature of journal articles and book chapters indexed in Scopus and Web of Science databases from 1991 to the end of September 2022. To this end, 505 core peer-reviewed journal articles and book chapters are compiled for evaluation after conducting forward and backward snowballing alongside removing irrelevant papers. This research study then exploits both VOSVIEWER and Bibiometrix R Package for the purpose of network visualization and scientometric mapping of the appended research studies. Thereafter, this paper carries out a multifaceted systematic review analysis of the identified literature covering tackled bridge defects, used non-destructive techniques, data processing methods, public datasets, key findings and future research directions. The present study is expected to assist practitioners and policymakers to conceive and synthesize existing research and development bodies, and future trends in the domain of the assessment of bridge defects using non-destructive techniques. It can also aid in raising awareness of the importance of defect management in bridge maintenance systems.

The construction of sustainable bridge projects has become a global mission and challenge in the 21st century. Unfortunately, there has been a rise in bridge collapse incidents due to various factors in recent years both during the construction and service phases. These incidents have resulted in significant loss of life and property damage, exacerbating the five sustainable development issues faced by bridge engineering: natural, resource, environmental, social, and economic factors. As a result, the prevention and resolution of bridge collapse accidents have garnered attention from professionals, research institutions, and government departments, making it a prominent research area. In line with the sustainable development concept of bridge engineering, this article classifies the causes of bridge collapses into two categories: those occurring during the construction phase and those happening during the service phase; the latter includes lack of inspection, maintenance and management, external natural factors, and human factors. Furthermore, this article thoroughly examines the existing national management framework, identifying the dilemmas that hinder its effectiveness in regulating bridge collapse prevention. Finally, several effective suggestions are proposed for the prevention of bridge collapse incidents. These recommendations aim to motivate governments, project owners, designers, constructors, managers, and users to actively develop and promote high-quality sustainable bridges.