Bridge Condition Rating Research Articles

Condition Rating Prediction for Highway Bridge Based on Elman Neural Networks and Markov Chains

Bridges are a critical component of transportation infrastructure, playing a vital role in connectivity. The safe operation of bridges demands significant resource and capital investment, particularly as the operation phase is the most extended period in a bridge’s life cycle. Therefore, the efficient allocation of resources and funds is crucial for the maintenance and repair of bridges. This study addresses the need to predict changes in bridge condition over time. The commonly used state-based Markov chain method for bridge condition rating prediction is straightforward but limited by its assumptions of homogeneity and memorylessness. To improve upon this, we propose a novel method that integrates an Elman neural network with a Markov chain to predict the bridge condition rating. Initially, the ReliefF algorithm conducts a sensitivity analysis on bridge features to obtain the importance ranking of these features that affect the bridge condition. Next, six significant features are selected for data classification: bridge age, average daily truck traffic volume, material type, skew angle between bridges and roads, bridge deck structure type, and bridge type. The Elman neural network is then trained to train a prediction model for bridge condition ratings using the classified data, which can predict the condition levels of bridges. The Markov chain’s transition probability matrix is derived using a genetic algorithm to match the deterioration curve predicted by the Elman neural network. This proposed method, when applied to actual bridge data, demonstrates its effectiveness as evidenced by the condition rating of an actual bridge.

Deep Learning-Based Steel Bridge Corrosion Segmentation and Condition Rating Using Mask RCNN and YOLOv8

The application of deep learning (DL) algorithms has become of great interest in recent years due to their superior performance in structural damage identification, including the detection of corrosion. There has been growing interest in the application of convolutional neural networks (CNNs) for corrosion detection and classification. However, current approaches primarily involve detecting corrosion within bounding boxes, lacking the segmentation of corrosion with irregular boundary shapes. As a result, it becomes challenging to quantify corrosion areas and severity, which is crucial for engineers to rate the condition of structural elements and assess the performance of infrastructures. Furthermore, training an efficient deep learning model requires a large number of corrosion images and the manual labeling of every single image. This process can be tedious and labor-intensive. In this project, an open-source steel bridge corrosion dataset along with corresponding annotations was generated. This database contains 514 images with various corrosion severity levels, gathered from a variety of steel bridges. A pixel-level annotation was performed according to the Bridge Inspectors Reference Manual (BIRM) and the American Association of State Highway and Transportation Officials (AASHTO) regulations for corrosion condition rating (defect #1000). Two state-of-the-art semantic segmentation algorithms, Mask RCNN and YOLOv8, were trained and validated on the dataset. These trained models were then tested on a set of test images and the results were compared. The trained Mask RCNN and YOLOv8 models demonstrated satisfactory performance in segmenting and rating corrosion, making them suitable for practical applications.

A deep learning-based approach for assessment of bridge condition through fusion of multi-type inspection data

Bridges typically undergo regular inspections to assess their structural conditions. However, relying solely on numerical data overlooks valuable information from other data types, reducing assessment reliability. Although data fusion is an effective solution, existing methods poorly handle defective data scenarios (sparse, imbalance, and loss). To address this issue, this study proposes a novel deep learning-based assessment model, the Bridge Information Fusion Network (BI-FusionNet). The features of the developed BI-FusionNet: (1) Feature extraction and processing layers can be compatibility with processing networks for various data types, extracting and unifying the key features of these data; (2) Innovative fusion technology combining SENet and the random fusion matrix, enabling deep fusion from data types to feature information. Appropriate extraction models mitigate sparse data effects via extracting critical features; the novel feature fusion strategy exploits cross-type information, resolving the imbalanced and missing data issues. The experimental results verified that the BI-FusionNet model achieved an accuracy of 0.9687 in assessing bridge conditions using normal dataset. In the effectiveness test, the model accuracy was 0.8377 by using the defective dataset, outperforming baseline methods. Therefore, the proposed BI-FusionNet can alleviate the issue of performance degradation from defective data and facilitates multi-type inspection data application in bridge condition evaluation.

Condition Assessment of Highway Bridges Using Textual Data and Natural Language Processing- (NLP-) Based Machine Learning Models

Condition rating of bridges is specified in many countries since it provides a basis for the decision-making of maintenance actions such as repair, strengthening, or limitation of passing vehicle weight. In practice, professional engineers check the textual description of damages to bridge members, such as girders, bearings, expansion joints, and piers that are acquired from periodic inspections, and then make a rating of the bridge condition. The task is time-consuming and labor-intensive due to the large amount of detailed data buried in the inspection reports. In this paper, a natural language processing- (NLP-) based machine learning (ML) approach is proposed for automated and fast bridge condition rating, which can efficiently extract the information of deficiencies in bridge members. The proposed approach involves three major steps, say, data repository establishment, NLP-based textual data processing, and ML-based bridge condition rating prediction. The data repository is established with the inspection reports of 263 concrete bridges, and in total there, are four condition levels for the bridges. Then, the NLP-based textual data processing approach is implemented to calculate the word frequency and the word clouds to visualize the characteristics of bridges in different condition levels. Finally, four typical ML techniques are adopted to generate the predictive model of the bridge condition rating. The results indicate that the NLP-based ML prediction model has an accuracy of 89% and is very efficient so that it can be used for large-scale applications such as condition rating for regional-level bridges.

Machine learning approach for predicting bridge components’ condition ratings

Information on bridge condition rating is critical to make decisions regarding rehabilitation or replacement of bridges. Currently, bridge components’ condition ratings are evaluated manually using inspection reports. Markov chain and Petri net models are most commonly used for predicting future values of bridge parameters, however, applicability of these models for a regional or statewide portfolio of bridges may be limited. The existing data based models have low prediction accuracy. Hence, a data and machine learning based approach is presented herein for predicting the future condition values of major components—deck, superstructure and substructure—in a portfolio of bridges with an objective to develop a more accurate approach. National Bridge Inventory (NBI) was used to get information on current and past bridge components’ condition from year 1992–2019 along with other parameters such as ownership, maintenance responsibility and age. After selecting important parameters, this data was used to train three RUSBoost based random forest models for predicting future values of deck, superstructure, and substructure conditions, respectively. The prediction accuracy of the developed models were found above 93%, thereby addressing the limitation of poor prediction accuracy of the existing studies. Additionally, the uncertainties associated with the random forest based predictions were quantified at the regional level and for individual bridges. On-system concrete pre-cast slab units and steel I-beam bridges in Louisiana were selected to demonstrate the proposed approach and predict bridge components condition ratings for years 2020 and 2021.

Multi-modal deep fusion for bridge condition assessment

Bridge condition rating is a challenging task as it largely depends on the experience-level of the manual inspection and therefore is prone to human errors. The inspection report often consists of a collection of images and sequences of sentences (text) explaining the condition of the considered bridge. In a routine manual bridge inspection, an inspector collects a set of images and textual descriptions of bridge components and assigns an overall condition rating (ranging between 0 and 9) based on the collected information. Unfortunately, this method of bridge inspection has been shown to yield inconsistent condition ratings that correlate with inspector experience. To improve the consistency among image-text inspection data and further predict the accordant condition ratings, this study first provides a collective image-text dataset, extracted from the collection of bridge inspection reports from the Virginia Department of Transportation. Using this dataset, we have developed novel deep learning-base methods for an automatic bridge condition rating prediction based on data fusion between the textual and visual data from the collected report sets.Our proposed multi modal deep fusion approach constructs visual and textual representations for images and sentences separately using appropriate encoding functions, and then fuses representations of images and text to enhance the multi-modal prediction performance of the assigned condition ratings. Moreover, we study interpretations of the deployed deep models using saliency maps to identify parts of the image-text inputs that are essential in condition rating predictions. The findings of this study point to potential improvements by leveraging consistent image-text inspection data collection as well as leveraging the proposed deep fusion model to improve the bridge condition prediction rating from both visual and textual reports.

A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

Integrating structural health monitoring (SHM) data into reliability assessment has increasingly been practiced in the condition evaluation of in-service bridges over the past decade. The selection of probability distribution models for load- and resistance-related random variables is a prerequisite for monitoring-based reliability assessment. However, the underlying probabilistic assumptions of the used models could be restrictive and unverifiable especially when dealing with real-world heterogeneous monitoring data, weakening the confidence on the estimated reliability index. This study aims to develop a nonparametric Bayesian model with the Dirichlet process prior for bridge reliability assessment, where the model order constraint can be released such that the complexity of the model adapts to the observed data. Reliability analysis via the nonparametric Bayesian model allows the aleatory uncertainty and the epistemic uncertainty arising from monitoring data to be concurrently accounted for in the formulated reliability index. A numerical example is presented to verify the effectiveness of the nonparametric Bayesian model for dealing with multimodal data. The feasibility of the proposed approach for reliability assessment is then demonstrated with one-year strain monitoring data acquired from a large-scale bridge instrumented with the SHM system.

Bridge Infrastructure Management System: Autoencoder Approach for Predicting Bridge Condition Ratings

Being an essential component of our economy, bridges play a vital role in facilitating transportation of people and goods all over the world. Bridge infrastructure systems help in developing an effective way to monitor and protect structures in all aspects. However, the bridges are exposed to various kinds of damages due to aging, heavy load of traffic, quality of construction, and so on. Hence, determining the condition of such bridges through a proper infrastructure management system is important for understanding the potential loss in the longevity of the structures. This research paper presents the development and evaluation of an autoencoder-random forest (AE-RF) model to predict the condition rating of bridges using the National Bridge Inventory (NBI) database. To demonstrate the proposed model, a case study using bridges present in the US state of Florida has been performed using historical NBI data (2011–2020). Through this research, it was identified that when one of the deep learning models, named autoencoder, is combined with random forest (RF), it results in an efficient model for determining the condition ratings of the bridge components with fewer input parameters. The developed model was about 90% accurate in predicting the bridge’s deck condition by using other rating values as input variables and about 79% accurate without the use of any other rating factors, thereby addressing the existing research gap in determining the condition rating without the use of historic condition rating. On the other hand, the model was 78% and 77% accurate in determining the condition ratings for superstructure and substructure without using historic ratings and evaluation parameters. Hence, the proposed model would be more reliable in the evaluation of condition of existing bridges across the nation.

Probabilistic Bridge Deterioration Prediction, Applied Analytics, and Predictive Modeling

Designing a new bridge structure or designing the rehabilitation of an existing bridge is a time-consuming process. It is important for bridge owners to proactively assess their structures to make sure that by the time rehabilitation or replacement is required, the design phase elements of such a project, including necessary permits, are ready. Assessing future bridge structure condition also helps the owner to evaluate a variety of design choices or options based on the risk imposed by each alternative. Federal regulations (MAP-21) have required state transportation agencies to implement asset management performance measures. Asset management performance strategies are arguably principal in guiding transportation infrastructure investments. Predicting future asset condition helps businesses and engineers to allocate and utilize the available resources in a direction that keeps the existing bridge inventory in the best possible condition, as well as serves the public. Prediction of bridge future condition also assists engineers and bridge owners in making decisions based upon quality information and well-defined and considered objectives [1]. The core element of any bridge management system is the database containing physical condition–rating data obtained through regular inspection and maintenance activities over a significant amount of time. Consequently, asset management relies on having an accurate inventory and condition assessment of assets in real time. One significant problem lies in the stochastic nature of traffic that transpires over time. This research focused on using the Markov process to predict future bridge condition state based on bridge inspection data inputs. Markov chain modeling was selected for this study because bridge condition ratings have a stochastic nature that requires an appropriate deterioration prediction method. The goal was to create a computational tool to predict bridge and highway degradation and assess such a structure’s long- term performance. As a proposed update to the National Bridge Inspection Standard, this study offers two methods for making changes to bridge inspection intervals. Both ways consider risk a significant contributing factor to the deciding criteria, for which this paper presents methodology to perform such a risk calculation..

A Comparative Study of Bridge Inspection and Condition Assessment between Manpower and a UAS

As the number of old bridges increases, the number of bridges with structural defects is also increasing. Timely inspection and maintenance of bridges are required because structural degradation is accelerated after bridge damage. Recently, in the field of structural health monitoring, a bridge inspection using an unmanned aerial vehicle system (UAS) is receiving a lot of attention. In this paper, UAS-based automatic damage detection and bridge condition evaluation were performed on existing bridges. From the process of preparing for inspection to the management of inspection data, the entire bridge inspection process was performed through field tests. The necessary element techniques for each stage were explained and the results were confirmed. Finally, UAS-based results were compared with conventional human-based visual inspection results. As a result, it was confirmed that the UAS-based bridge inspection is faster and more objective than the existing technology. Therefore, it was confirmed that the automatic bridge inspection method based on unmanned aerial vehicles can be applied to the field as a promising technology.

Evaluation Of Post Tensioned Bridges’ Tendon Ducts By NDT And Minor Invasive Measures

This presentation introduces the author’s accumulated long experience in applying state of the art NDT methods for post tensioned bridges’ grout injection evaluation together with minor invasive investigations for validating NDT results and for evaluation of prestressing system condition including duct’s grout injection and prestressing steel corrosion. The injection grout (cementitious based grout) has a crucial role in protecting the prestressing steel against corrosion caused by different environmental stresses. Corrosion of prestressing steel may cause dangerous fracture mechanisms such as hydrogen embrittlement and stress corrosion cracking. In addition, the injection grout has the important role of intermediating the prestressing forces from the prestressing steel to the concrete structure. Therefore, successful grout injection is critical for the correct structural functionality of a post tensioned bridge. The introduced NDT methods (ultrasound tomography and Impact-Echo), used together with invasive investigations, have already been successfully applied by the author in over 110 inspections of post tensioned bridges in the past 12 years, mostly in Finland. In situ evaluation of NDT testing results pinpoint the logical locations for invasive investigations. These include opening of tendon ducts and evaluation of the steel duct, injection grout and condition of the prestressing steel (ocular corrosion evaluation). There are thousands of aging post-tensioned bridges, frequently on critical transportation routes, built mostly in the 1960s-1970s. Occasionally the prestressing was applied in a faulty manner due to various reasons. In some cases, when stress from the environment is added to faulty construction, the consequences may be severe and possibly affect the safe usage of bridges. Faults and damages in prestressing systems are hidden from the eyes. Therefore the severity of the situation is unknown and it may result in extreme cases even in sudden collapse of bridges - as witnessed in the 2018 catastrophic collapse of the Morandi Bridge in Genoa Italy where apparently, according to publications, a post tensioned main structural part failed. Significant part of the aged post tensioned bridges have already reached renovation time. Therefore, their condition should be assessed. Bridge condition evaluation usually involves “traditional” inspection techniques which do not provide information on the internal prestressing system even though it has a crucial role in the bridge load carrying capacity. The abovementioned NDT methods combined with invasive investigations enable this critical evaluation task. In addition, those abovementioned NDT methods are suitable as quality assurance measures in newly constructed post tensioned bridges for ensuring long service life of bridges since day one.

Developing Bridge Deterioration Models Using an Artificial Neural Network

The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands of bridges. The inability to obtain funding for the current infrastructures, such that they comply with the requirements identified as part of maintenance, repair, and rehabilitation (MR&R), makes such bridge management systems critical. Bridge management systems facilitate decision making about handling bridge deterioration using an efficient model that accurately predicts bridge condition ratings. The accuracy of this model can facilitate MR&R planning and is used to confirm funds allocated to repair and maintain the bridge network management system. In this study, an artificial neural network (ANN) model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures. A large dataset of historical bridge condition assessment data was used to train and test the proposed ANN models for the deck, superstructure, and substructure components, and the accuracy of these models was 90%, 90%, and 89% on the testing set, respectively.

Data-driven structural condition assessment for high-speed railway bridges using multi-band FIR filtering and clustering

The monitoring data makes it feasible to inspect and assess the structural condition of bridges in real time. Among the diverse in situ data of high-speed railway bridges under varying operational environments, dynamic responses caused by passing trains can offer insight into the mechanical properties of the bridge structure. Based on the train-induced response features of influence line (IL) and dynamic load factors (DLF) extracted from raw measured data, a comprehensive data-driven approach is developed for structural condition assessment of high-speed railway bridges, which is applied to a long-span steel-truss bridge as a validation. Considering the sparsity of the train-induced static response in the frequency domain, the multi-band finite impulse response (MFIR) filtering is used to extract the train-induced response features. The features are clustered via the Gaussian mixture model (GMM), and the two-level objective for structural condition evaluation and degradation warning of bridges can be achieved through the dynamically updated clustering results and probabilistic models. The results demonstrate that (1) MFIR filtering can effectively reject abnormal or interfering data and accurately extract the train-induced response features, and (2) the intrinsic nature and laws of features can be revealed by GMM clustering, which provides a statistical premise for the reliability analysis of bridge structures.

An improved micro model for stochastic traffic load simulation with consideration of axle load and microscopic behavior

Simulating the stochastic traffic load has been an important issue for bridge condition evaluation. This paper proposes an improved cellular automaton for stochastic traffic load simulation with consideration of axle load and microscopic interaction. The cells, neighbors, transition rules and boundary conditions in the improved model are all redefined. The cell length can be manually defined with any value. The vehicle load can be precisely simulated using axle load if the cell length is set small or using concentrated load when a large cell length is defined. Acceleration based transition rules are proposed, and different acceleration for each vehicle can be computed to more reasonably simulate the microscopic behavior. The feasibility of the proposed model is validated through comparisons on traffic flow simulation and load effect quantification with in-field weigh in motion data and other models. The results indicate that the proposed model improves the existing models both in vehicle load simulation and traffic flow simulation and can be used for stochastic traffic load simulation for bridges with any length.

Research on Engineering Geomechanics Characteristics and CFRP Reinforcement Technology Based on Machine Learning Algorithms

We have completed the design of an early warning and evaluation analysis module based on machine learning algorithms. Aiming at the prestressed CFRP-strengthened reinforced concrete bridges under natural exposure, we developed a theoretical model to analyze the long-term prestress loss of reinforced parts and the adhesion behavior of the CFRP-concrete interface under natural exposure conditions. The analysis deeply reveals the technical and engineering geomechanics characteristics of the D bridge. At the same time, through a series of experimental studies on the D bridge condition monitoring system, the data acquisition and transmission, processing and control of the D bridge condition monitoring system, and the bridge condition monitoring and evaluation software are provided. Regarding how to repair the engineering geomechanical characteristics of D bridge, we mentioned the prestressed CFRP reinforcement technology. The prestressed carbon fiber reinforced composite (CFRP) structure made of reinforced concrete (RC) makes better use of the high-strength characteristics of CFRP and changes. It strengthens the stress distribution of the components and improves the overall strength of the components. It is more supported by engineers in the civil engineering and transportation departments. However, most prestressed CFRP-reinforced RC structures are located in natural exposure environments, and the effect of natural exposure environments on the long-term mechanical properties of prestressed C FRP-reinforced RC components is still unclear. This article mainly uses the research on the engineering geomechanics characteristics and reinforcement technology of the bridge body, so that people have a deep understanding of its concept, and provides reasonable use methods and measures for the maintenance and protection of the bridge body in the future. This paper studies the characteristics of engineering geomechanics based on machine learning algorithms and applies them to the research of CFRP reinforcement technology, aiming to promote its better development.

Evaluation of the Consistency of Bridge Inspection Ratings in New York State

Previous studies by the Federal Highway Administration (FHWA) have shown significant variability in the rating of bridge elements through visual inspection. In this study, a detailed evaluation of the consistency of inspection ratings of four representative bridges in New York State has been conducted, using inspection data reported by 21 inspection teams employed by the New York State Department of Transportation (NYSDOT). The bridge condition rating scale of NYSDOT ranges from 7 to 1, with 7 being new and 1 being in a failed condition. In addition, the NYSDOT assigns ratings 8 and 9 to cases “not applicable” and “condition and/or existence unknown,” respectively. In general, consistency is defined as “conformity with practices and procedures.” In this study, consistency of inspection of elements has been quantified as a percentage of inspectors that rated the bridge element within ±1 of the median rating for elements with ratings 7 to 1, or as a percentage of inspectors who have assigned a rating of 8 or 9 to elements expected to be rated 8 or 9. Based on the evaluation of the consistency of elements of the four bridges in the study, the results show that 96% of the elements have a consistency of at least 90%. Only seven elements have been identified to have a consistency of less than 90%. For elements whose rating was supposed to be 8, approximately 97% of the elements had a consistency of at least 90%, whereas, for elements whose rating was supposed to be 9, 91% of the elements were inspected with a consistency of at least 95%. Overall team consistency based on inspection of all elements was approximately 98%. The results imply that the NYSDOT bridge inspection and training programs are effective, as evidenced by the reporting of consistent bridge ratings by multiple inspection teams.

Knowledge-Based Fuzzy Expert System to the Condition Assessment of Historic Road Bridges

A systematic methodology for condition assessment of the historic road bridges was needed because of the poor and inadequate condition of bridges which cannot satisfy everyday-day dynamic loads and deteriorations due to the aging process. Thus in this study, a new expert system based on the knowledge approach has been proposed to develop a systematic procedure for condition assessment of these bridges using fuzzy logic and sets of α-cuts. Each bridge is divided into three components: superstructure, substructure, and equipment, and each component is divided into relevant elements. These elements are evaluated by an expert and their ratings are fuzzified according to defined fuzzy sets, their membership functions, and linguistic values. Furthermore, fuzzy structural importance is given to ratings of each element. Combinations of these two values are calculated to obtain a fuzzy rating of the component using the Fuzzy Weighted Geometric Mean (FWGM). Finally, for the defuzzification of the component rating, the centroid method is proposed. The Analytic Hierarchy Process (AHP) is used for comparison of the components. The bridge condition rating is achieved by summering all the components ratings multiplied by their relative importance, and it is presented as a value of the Historic Road Bridge Condition Assessment Index (HRBCAI). The validation is conducted on the bridges built until the end of the Austro-Hungarian Monarchy in Split-Dalmatia County, Croatia.

LR-BCA: Label Ranking for Bridge Condition Assessment

Bridge condition assessment (BCA) plays an important role in modern bridge management. Existing assessment methods are time-consuming, labor-intensive and error-prone. The use of machine learning for BCA can effectively solve the above problems. However, the large amount of label noise in the dataset severely affected the performance of the BCA model. In this paper, we present an effective label ranking approach for BCA (LR-BCA). Our proposed LR-BCA method considers the natural order relationship between bridge condition ratings. Moreover, a heuristic data cleaning (HDC) approach is proposed for cleaning bridge condition dataset. The HDC method firstly identifies all the label conflict examples, then iteratively filters out the noise. Experimental results on real-world dataset confirm the effectiveness of the HDC method and demonstrate that our proposed LR-BCA method achieves 99% Top-2 accuracy, which is highly competitive compared to baseline methods.

Applicability of Fuzzy-Based Visual Inspection Approach for Condition Assessment of Bridges in Developing Countries: A State-of-the-Art Review

Visual inspection method is a widely used technique for periodically assessing the condition of existing bridges. The visual inspection method is simple, easy to use and requires minimum investment. As a result, it is particularly suitable for developing countries where paucity of fund and availability of advanced equipments for condition assessment of bridges are limiting factors. However, the effectiveness and reliability of the assessment depend largely on factors such as the technical competence of the inspector, his/her experience, perception, judgment, etc. Such subjectivities, inherent in visual inspection method, may however be addressed by fuzzy approach. This technique has the capability to analyze a complex system, inherent with uncertainties, using approximate reasoning which is neither exact nor inexact. Therefore, fuzzy logic-based visual inspection approach couples the simplicity and economy of visual inspection method and mathematical rigor of fuzzy logic to minimize the uncertainty and unreliability of visual inspection data. As a result, it has the potential to be used as an effective tool for condition assessment and rating of bridges in developing countries. Hence, in this paper an effort is made to review the existing literature on this technique and its components under a few broad categories and discuss the advantages as well as limitations of each of them. Finally, knowledge gaps and challenges are discussed to motivate future research in this field for practical implementation in developing countries.

Inspection and evaluation of a network of concrete bridges based on multiple NDT techniques

Bridge condition rating is a crucial variable used for prioritisation of bridge needs in bridge management systems (BMSs). At present, bridge rating is predominantly based on visual inspections. A practical method incorporating non-destructive testing (NDT) results for rating bridges in a BMS is not available. In this study, an integrated rating method for reinforced concrete bridges based on both visual inspection and multiple NDT techniques with the ultimate goal of incorporating it into BMSs was developed. The method uses a multi-component and multi-modal NDT application at network-level with improved testing speed. The application results of four non-destructive techniques including visual inspection, polarisation resistance, penetration resistance, and pulse velocity on a reinforced concrete bridge network of 100 bridges are presented. At bridge level, a multi-component approach was used, including decks, girders, cap beams, piers, and abutments. A novel method for NDT-based reinforcement corrosion rating was developed. Correlation among NDT variables was also investigated and the results are presented. Multiple linear regression models were developed using ANOVA analyses. Only the statistically significant regressors with the largest partial F-statistic were included in the model formulas. The expected weights of the four evaluation criteria in the formula were obtained by combining the results of two techniques: statistical data available in literature on the frequency of use of NDT techniques for bridge inspection, and a three-level analytical hierarchy process (AHP) based on expert survey. Application of three NDT techniques to inspections of concrete bridges, determination of NDT ratings using the integrated rating formula, and incorporation of the resulting ratings into a BMS for bridge ranking can be performed using the proposed methodology.