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Probability-based damage thresholds for bridges’ inspection-based deterioration curves
Abstract
A probability-based procedure is presented to define damage thresholds of bridges’ deterioration curves, which is applied to a large bridge database of real on-site inspections. The procedure is based on a change of variable to transform any distribution of bridge durability deterioration ratings into a normal distribution. This allows probability-based calculations of damage thresholds associated to certain criteria (e.g. Serviceability or Ultimate Limit State), for immediate application to deterioration prediction curves. This research defines a novel procedure for a probability-based analysis of inspection-based data and, de facto, provides a reliable statistical support system to traditional infrastructure management systems of bridges.
To investigate the dynamic characteristics of simply-supported beam bridges in high-speed railways under long-term deterioration, the deterioration law of concrete strength, considering the combined effects of carbonation and freeze-thaw cycles, is analyzed based on time-varying concrete strength theory. The accuracy of the finite element model is validated by measuring and comparing the vibration signals of a two-lane simply-supported beam bridge on the Beijing-Shenyang high-speed railway. Subsequently, the frequency variability of mid-span box girder vibration signals and vibration energy is analyzed using local maximum synchrosqueezing transform, and the multifractal characteristics of the box girder vibration signals are further examined. The results indicate that vertical acceleration of the box girder and the top of the pier increased by 31.2% and 67.6%, respectively, as deterioration intensified. Additionally, the vibration frequency of the box girder decreased from 28 Hz to 25 Hz, suggesting a marked decline in structural stiffness. Simultaneously, the energy of the self-oscillating frequency near 8 Hz dispersed progressively over time. The width (Δ α ) of the multifractal spectrum increases gradually, reflecting more pronounced multifractal characteristics, while the waveform of the box girder vibration signals shifts from small to large wave peaks. These findings provide valuable insights for performance evaluation, safety monitoring, and maintenance of simply-supported beam bridges.
The paper proposes a method for modal-based damage assessment in structures where historical data on their modal properties is often missing. This method is based on the measurements of the structure's eigenfrequencies and predefined assumed damage states. Damage is defined at the level of confirming damage existence with a rough estimate of its extent, within the limits of the predefined assumptions. The factors that affect the outcomes of experimental modal analysis of damaged reinforced and prestressed beam elements on structures in use are briefly outlined and the challenges encountered during these analyses and potential solutions are briefly discussed. The method's application is demonstrated on a damaged prestressed concrete simple beam. Bayesian formulation for probability estimation is used to calculate the probability that a beam, characterized by a specific measured natural frequency, is either undamaged or has a certain level of damage. This calculation is based on the results from finite element models created in the Abaqus software suite. In the finite element models, an eigenfrequency distribution, based on the modulus of elasticity distribution, of a prestressed beam is obtained, for different levels of the assumed prestressing force drop and the corresponding damages caused by the force drop. In the presented problem, the modulus of elasticity of concrete is incorporated into the analysis as an uncertain parameter with a normal distribution. Similarly, other uncertain parameters of the actual structure can be modelled.
The inspection of underwater structural health is crucial for comprehensive bridge health assessments. In underwater structure imaging, traditional methods include single-camera and binocular camera inspection. However, due to water turbidity and long working distances with a small field-of-view, obtaining clear and high-quality detection images with these methods takes much work. To address this problem, this paper presents a method for planar array image stitching based on Harris corner point extraction, utilizing the advantages of planar array cameras characterized by short working distances and wide field-of-view. The core contribution of this paper is the introduction of an innovative image sequence stitching algorithm utilizing Harris corner point extraction and the combination of the first proposed planar array cameras with the image sequence stitching algorithm, which solves the problem of long distance and small field-of-view during the underwater inspection. The image stitching method involves calibrating camera parameters with a checkerboard and stitching underwater images from planar array cameras to reveal underwater structural features. Furthermore, five quantitative evaluation metrics and the method for calculating the field-of-view loss rate are presented to evaluate and analyze the stitched images. A series of experiments were performed on concrete surfaces, aquatic and underwater, with a total field-of-view of the underwater image after stitching of 358.86 mm × 319.24 mm at a working distance of 160 mm. Five evaluation methods were used to quantitatively evaluate the quality of the stitched images and calculate the field-of-view loss rate of the images. The results indicate that the proposed method improves the ability to inspect underwater. The stitched images achieve notable metrics: an entropy of approximately 6.7, an average gradient of about 1.7, a spatial frequency of around 3.5, an edge strength of about 17, mutual information of approximately 1.2, and a field-of-view loss rate of <0.1, facilitating more effective underwater structure inspection.
Nowadays, an increasing number of reinforced concrete (RC) bridges require refurbishment and repair works due to their advanced deterioration. However, the level of damage that RC structures may experience due to the action of different aggressive agents is not always evident to the naked eye, representing risks for bridges’ owners, concessionaires, and users.
Whereas the control of the mechanical deterioration and damage of bridges has reached a high level of development in recent years, the monitoring of the deterioration due to physical, chemical, or biological processes is currently in a less developed stage. For instance, corrosion and carbonation evolution rates of existing structures depend on different threshold values that may differ significantly from the standard values proposed for design purposes of new constructions. Moreover, these threshold values depend on several parameters such as temperature, type of cement, or environmental conditions (e.g., dry-wet cycles). Therefore, durability prediction models must also include an assessment of the evolution of the structural performance exposed to the aggressive actions of various agents simultaneously.
During the last decades, many administrations have been implementing Bridge Management Systems (BMS) to control their infrastructures through periodic inspections to detect damages and reduce the risk as well as the uncertainty about the level of investment required to repair a certain structure. The collection of these data results in inspection databases which include large amounts of information that can be used to understand how RC structures deteriorate under different environmental aggressiveness conditions.
This paper focuses on the definition of criteria for the implementation of an inspections-based BMS for RC bridges. For the purpose of this paper, since extensive inspections databases are needed to perform the analyses described, real data from the ‘AURA’ BMS have been used, which includes a database of hundreds of Spanish RC bridges’ inspections.
During the last decades, an increasing number of administrations have been implementing Bridge Management Systems (BMS) to control their infrastructure and detect damages that may require reparation or further analysis. The collection of these data results in inspection databases which include large amounts of information that can be used to understand how reinforced concrete (RC) structures deteriorate over time under different environmental aggressiveness conditions. Based on collected information of 298 roadway RC bridges, this paper demonstrates how the statistical analysis of the data obtained from inspections can be used to predict their ageing evolution over time in a reliable way.
Structural reliability has become a widely accepted performance indicator for infrastructures over the past decade, providing valuable information about their structural condition. As a result, it has been assessed in combination with deterioration prediction, aiming at defining optimal maintenance, and rehabilitation strategies for bridge networks. In that case, reliability values need to be updated based on collected data. To this purpose, there has been a rapid development of advanced bridge condition assessment techniques, both in the fields of structural health monitoring as well as on non-destructive assessment techniques. Most of the sophisticated non-destructive methods are the preferred option but sometimes are not possible. Thus, visual inspection is still the predominant bridge condition assessment technique being adopted within the majority of Bridge Management Systems (BMS). However, there is a procedural gap when incorporating information obtained from visual inspections into a reliability assessment. Therefore, this paper describes a methodology for a time-dependent reliability-based condition evaluation of existing bridges. The procedure is applied to a pre-stressed reinforced concrete railway bridge located in Portugal, in which prediction of reliability levels are calculated for different periods assuming corrosion initiation, causing a reduction in the cross-section area of the steel reinforcement and residual strength reduction, based on onsite inspection evidence. Finally, the updating is made through a Bayesian approach to compute the posterior bridge reliability based on inspection results. This approach may apply to other types of structures considering information obtained from visual inspection concerning the actual deterioration state in a quantitative way.
To assess the reliability of a structure, reliability based codes such as EN1990:2002 allow for the application of full-probabilistic methods and semi-probabilistic methods (i.e. the partial factor method). In principle, both methods should be equivalent and lead to (approximately) the same reliability level. In this study, this equivalence is assessed by investigating the structural reliability of a large number of structural elements designed according to EN1990:2002 partial factor method. Several material types, failure modes, variable loading types and load ratios are investigated. Both the one year reference period and the fifty year reference period are assessed and compared. For the future developments of EN1990, several suggestions are made to obtain a more uniform reliability level over different load-ratios between self-weight, permanent loads, and variable loads. Most important recommendations for EN1990 are: (i) to switch to the one year reference period for the reliability based design and assessment of structures and structural elements; (ii) to lower the partial factor for self-weight for material types with low variability; (iii) to include a slightly larger partial factor for wind and snow loads. It was demonstrated that neither of these suggestions would require any change or recalibration of the material dependent Eurocodes.
Bridge Management Systems (BMS) are decision support tools that have gained widespread use across the transportation infrastructure management industry. The Whole Life Cycle Cost (WLCC) modelling in a BMS is typically composed of two main components: a deterioration model and a decision model. An accurate deterioration model is fundamental to any quality decision output.
There are examples of deterministic and stochastic models for predictive deterioration modelling in the literature, however the condition of a bridge in these models is considered as an ‘overall’ condition which is either the worst condition or some aggregation of all the defects present. This research proposes a predictive bridge deterioration model which computes deterioration profiles for several distinct deterioration mechanisms on a bridge.
The predictive deterioration model is composed of multiple Markov Chains, estimated using a method of maximum likelihood applied to panel data. The data available for all the defects types at each inspection is incomplete. As such, the proposed method considers that only the most significant defects are recorded, and inference is required regarding the less severe defects. A portfolio of 9,726 masonry railway bridges, with an average of 2.47 inspections per bridge, in the United Kingdom is the case study considered.
Bridge deterioration models are used for prioritization and maintenance of bridges. These models can be broadly classified as deterministic and stochastic models. There are mechanistic models (or physical models) as well as artificial intelligence (AI)-based models, each of which can be stochastic or deterministic in nature. Even though there are several existing deterioration models, state-based stochastic Markov chain-based model is widely employed in bridge management programs. This paper presents a critical review of different bridge deterioration models highlighting the advantages and limitations of each model. The models are applied to some case studies of timber superstructure and concrete bridge decks. Examples are illustrated for arriving at bridge deterioration models using deterministic, stochastic and artificial neural network (ANN)-based models based on national bridge inventory (NBI) data. The first example is based on deterministic model and the second on stochastic model. The deterministic model uses the NBI records for the years 1992–2012, while the stochastic model uses the NBI records for one year (2011–2012). The stochastic model is state-based Markov chain model developed using transition probability matrix (TPM) obtained by percentage prediction method (PPM). The two deterioration models (i.e., deterministic and stochastic models) are applied to timber highway bridge superstructure using NBI condition data for bridges in Florida, Georgia, South Carolina and North Carolina. The illustrated examples show that the deterministic model provides higher accuracy in the predicted condition value than the stochastic Markov chain-based model. If the model is developed based on average of transition probabilities considering the data for the period 1992 to 2012, the prediction accuracy of stochastic model will improve. Proper data filtering of condition records aids in improving the accuracy of the deterministic models. The third example illustrates the ANN-based deterioration model for reinforced concrete bridge decks in Florida based on the NBI condition data for the years 1992–2012. The training set accuracy and testing set accuracy in the ANN model are found to be 91% and 88% respectively. The trained model is utilized to generate missing condition data to fill the gaps due to irregular inspections of concrete bridges. This paper also discusses scope for future research on bridge deterioration modeling.
Structures made of structural concrete are designed to withstand different time‐dependent or time‐independent influences during their expected service lifetime. Most of these influences can be considered in a sufficient manner by mathematical and statistical approaches, while some do appear combined with certain structural, environmental, and material boundary conditions. These can, in most cases, not be foreseen, verified or they change during the lifecycle of a concrete structure. It is therefore obligatory to rely on an efficient conservation strategy for each structure to ensure its safety and overall economic efficiency. For this purpose, the fib Model Code for Concrete Structures 2010 already suggests a general workflow to follow a predefined or provisional conservation strategy and condition control procedure. These existing proposals are yet of very general nature and disclaim practical decision‐making rules to survey and assess existing structures as part of a condition‐based conservation strategy. In this contribution, the main decision‐making options at an early stage of a construction's life cycle management are pointed out, considering their importance for a structure's time‐dependent performance and their relation to continuative decisions for conservation management. Proposals for the implementation of practical advice for condition‐based conservation strategies in international guidelines are made.
In many areas of science, including business disciplines, statistical decisions are often made almost exclusively at a conventional level of significance. Serious concerns have been raised that this contributes to a range of poor practices such as p‐hacking and data‐mining that undermine research credibility. In this paper, we present a decision‐theoretic approach to choosing the optimal level of significance, with a consideration of the key factors of hypothesis testing, including sample size, prior belief, and losses from Type I and II errors. We present the method in the context of testing for linear restrictions in the linear regression model. From the empirical applications in accounting, economics, and finance, we find that the decisions made at the optimal significance levels are more sensible and unambiguous than those at a conventional level, providing inferential outcomes consistent with estimation results, descriptive analysis, and economic reasoning. Computational resources are provided with two R packages.
The present study provides a review of the most diffused technical and non-technical performance indicators adopted worldwide by infrastructure owners. This work, developed within the European COST Action TU 1406—“Quality specifications for roadway bridges, standardization at a European level,” aims to summarize the state-of-art maintenance scheduling practices adopted by bridge owners, mainly focusing on the identification and classification of the most diffused performance indicators (PIs). PIs are subdivided in technical and non-technical ones: for this latter subclass, PIs are classified in environmental, social and economic-targeted. The study aims to be a reference for researchers dealing with performance-based assessments and bridge maintenance and management practices.
Typical design of reinforced concrete structures is governed by the ultimate limit state (ULS), while serviceability limit state (SLS) is checked. The target reliability index for ULS is recommended based on cost optimization and back calibration of existing practice. Many design cases, such as prestressed concrete, are, however, governed by serviceability conditions and the background for the current recommendations for target reliability index of irreversible SLS as provided by ISO 2394 is unclear. This contribution reviews previously developed generalized framework for determination of SLS target reliability by taking into account a cost ratio and statistical parameters of action‐resistance variable. Target reliability index βt,SLS can be then obtained from the previously introduced table with a minimum effort. A case‐specific approach to the cost optimization with FORM analysis is introduced in order to validate the results of the generic approach. It is found that there is a discrepancy in terms of the obtained value of target reliability index. This can be attributed to the influence of the decision parameter d in the cost optimization process. It affects the probability of failure pf(d) at the different rates for different formulations of SLSs. The nearly linear assumption in the generic formulation may not necessarily apply in other cases, and care should be taken when specifying target reliability index βt,SLS as the effectiveness of the decision parameter must be observed.
Bridges begin to deteriorate as soon as they are put into service. Effective bridge management requires sound understanding of the deterioration mechanism as well as the expected service life. Decision makers design and execute programs that safely maintain or extend bridge service life at low cost. Key maintenance practices include inspections, repairs, and rehabilitation, among which inspections may be the most important since the other maintenance interventions are conducted based on the inspection report. This paper presents a methodology for determining the expected service life of a bridge or a bridge network based on a deterministic condition-based model associated with inspection quality. This study used almost 10-year condition rating results of bridges and developed a non-linear regression model that takes into account the Inspection Reliability Index (IRI). The IRI represents the relative inspection accuracy with respect to a tolerable error of one grade by comparing condition ratings between routine and in-depth safety inspections. In the evaluation of the IRI, significant variability in condition ratings between routine and in-depth safety inspections was observed. Specifically, approximately 42% of routine inspection results deviated within ± 1.0 grade with respect to results from in-depth safety inspections. A modified non-linear regression model combined with the IRI is proposed to predict the remaining service life of a bridge. The proposed model can estimate the service life of a bridge by combining the bridge age and its condition rating.
The organisations responsible for managing bridge assets in the United Kingdom collect large quantities of data on their bridges. A primary aspiration in the collection of asset data is that it can be processed into useful information that can inform decisions about future management of structures and enhance industry best practice. To enable this, bridge managers must take care to specify appropriate parameters to be recorded, in conjunction with a practical recording interval. In addition, the design of data collection and recording processes is key to ensuring that the data obtained can be transformed into useable information. This study draws on perspectives from a series of interviews with key agents involved in the management of bridges in the United Kingdom. The paper explores the nature of the data that is collected, and how this data is currently used.
The cause for deterioration of the concrete structure located in severe environment has been explored both in field and in laboratory. Serious cracking and spalling appeared upon surface of the concrete structure soon after the structure was put into service. Both alkali-aggregate reaction and freeze-thaw cycles may result in similar macro visible cracking and spalling. The possibility of alkali-aggregate reaction was excluded by both field survey and lab examination such as chemical analysis, petrographic analysis, and determination of alkali reactivity of aggregates. According to results of freeze-thaw cycles, impermeability testing, and microstructure analysis, it is deduced that the severe environmental conditions plus the relatively inferior frost resistance cause the deterioration of concrete. Usage of air entraining admixture can improve frost resistance and impermeability. Furthermore, new approaches to mitigate the deterioration of concrete used in severe environmental condition are discussed.
For European Industry, Consultants and Scientists in Industrial Safety and Life Cycle Engineering
Technologies have been developed for a competitive and sustainable European industry. This book reports about the state of science and technology in Industrial Safety and Life Cycle Engineering. It covers the following content:
- The new IRIS risk paradigm for industrial risk management
- Life Cycle Engineering for condition assessment and extension of lifetime
- Methodologies for risk identification, assessment and control
- Knowledge and technologies for the quantification of risk and its respective reduction
- Online monitoring and decision support systems to reduce uncertainties in the process
- Pattern recognition in signal processing for monitoring of key performance indicators
- A standardized degradation law and application to various case studies
Innovations that will allow operating Europe’s industry in a safer but as well more economic process are presented.
In the frame of research activities of the Department of structures and bridges, the modified reliability levels for existing bridge evaluation were derived. These levels were used for determining the partial safety factors for materials. Moreover, the partial safety factors of steel and concrete were determined depending on the age of the bridge and on the remaining lifetime of the bridge. New modified reliability levels for evaluation of existing bridges affect also the partial safety factors of loads. In the paper, the modification of reliability levels recommended according to Eurocode for bridge members subjected to bending are presented. These adjusted reliability levels should be used for existing bridge evaluation.
The European infrastructure asset has developed historically and is characterized by nation-specific construction processes. Inspection, condition assessment, and maintenance procedures differ from country to country. Because of historical and political circumstances, national infrastructure assets are maintained at different levels, too. Since the budget for maintaining the bridge infrastructure less and less meets the demands of a growing bridge stock, bridge inspection, maintenance, and life-cycle considerations gain higher importance. The need exists to develop effective diagnosis tools for early detection of construction faults, detects, and deterioration processes during inspection, to keep the bridge infrastructure at an acceptable level, from structural safety and economic viewpoints. An overview on the latest research projects and integrated bridge management systems in Europe is given. The potentials of nondestructive testing (NDT) are presented, with special focus on technical advances of NDT applications to reinforced concrete (RC) and posttensioned concrete bridges. Although NDT is not regularly integrated in these processes, the application brings valuable information on the current condition of the inner structure in called-in special inspections. NDT-automation and the application of imaging echo methods, combined with advanced data processing, produce a surprising level of information about the inner structure of massive RC slabs up to a depth of about 60 cm. Detected inhomogeneity and scatterers of acoustic or electromagnetic waves can be visualized in vertical or horizontal slices through the structure or animations. The fusion of different three-dimensional data sets of processed data improves the interpretability and accuracy of the results.
This paper describes methods for determining and utilizing Markov chains in the evaluation of highway bridge deterioration. Using a data base of 850 bridges in New York State, Markovian transition matrices (MTM) are first found for the overall bridge condition. Then, transition matrices are developed for the condition rating of individual bridge components (e.g., superstructures, decks, and piers). In each case, chains are determined for various types of construction. Also discussed is the modeling of correlated elements such as the primary structure and joint condition and the ability to determine the correlation for a set of data. The consequence of small data bases is discussed, and an explanation is offered for unexpected values of the transition probabilities. Finally examined is the use of Markovian analysis for predicting the evolution of the average condition rating of a set of bridges, and expected value of condition rating for a single bridge. Markov transition matrices are introduced to model the effects of repairs and to determine repair policies that will lead to constant average condition rating.
This paper presents some experimental data obtained from UHPFRC (Ultra-High Performance Fibre-Reinforced Concrete) being exposed for 10 years in a cooling tower and a high slag content concrete being exposed for 30 years in a marine environment. Experimental data are then used for assessing concrete durability through a theoretical approach, namely performance-based analysis. The results from the application of this approach are consistent with the penetration depth of aggressive agents measured from core samples. Finally a simulation method currently being developed by EDF is presented, which has great relevance to durability assessment.
Routine inspection is the most common type of highway bridge inspection completed to satisfy the requirements of the National Bridge Inspection Standards. Routine inspections of highway bridges are typically completed using only visual inspection (VI) and rely heavily on subjective assessments made by bridge inspectors. Given this practice and the fact that VI may have limitations that affect its reliability, the Federal Highway Administration completed a comprehensive study to examine the reliability of VI as it is currently practiced in the United States. The accuracy and reliability of condition ratings generated through routine inspections of six in-service and decommissioned highway bridges were studied. These timed inspections were completed in normal summer weather conditions under direct observation. To ensure that results from this study would be applicable to normal bridge inspections, state department of transportation bridge inspectors made up to the study sample, which included 49 inspectors from 25 states, representing a diverse cross section of the bridge inspector population. Results of the study indicate that routine inspections are completed with significant variability. Specifically, 95 percent of primary bridge element condition ratings will vary within two rating points of the average, and only 68 percent of these ratings will vary within one rating point. Additionally, the National Bridge Inspection Standards definitions of condition rating may not be sufficiently refined to allow for reliable routine inspection results.
This paper presents an experimental study to evaluate the influence of physical properties of aggregates on the durability of concrete. Different granite and limestone materials from various locations in Portugal were characterized and used as aggregates to produce several concrete mixes, with the same workability and volume proportions. The durability properties of these concrete mixes, produced with different aggregates, were determined. Test procedures and results obtained are discussed. The influence of aggregate types on concrete durability parameters is discussed.
Because no procedures exist to inspect and evaluate the condition of single-ply membrane roofing systems within the ROOFER program, the U.S. Army Construction Engineering Research Laboratories (USACERL) has developed this inspection and distress manual for these roofing systems. Included is the standardized information needed to conduct the visual inspection survey, including names, descriptions, severity levels, measurement criteria, causes and photographs of membrane and flashing distresses. Procedures for distress density calculations are also provided. Roof inspectors can use this information to objectively determine the indexes that reflect the (1) ability of the membrane and flashing to perform their functions, (2) needed level of maintenance, and (3) waterproof integrity.
Reliability-based design concepts and their application to load and resistance factor design (LRFD or limit states design (LSD) in Canada) are well known, and their adoption in geotechnical engineering design is now recommended for many soilstructure interaction problems. Two important challenges for acceptance of LRFD for the design of reinforced soil walls are (i) a proper understanding of the calibration methods used to arrive at load and resistance factors, and (ii)the proper interpretation of the data required to carry out this process. This paper presents LRFD calibration principles and traces the steps required to arrive at load and resistance factors using closed-form solutions for one typical limit state, namely pullout of steel reinforcement elements in the anchorage zone of a reinforced soil wall. A unique feature of this paper is that measured load and resistance values from a database of case histories are used to develop the statistical parameters in the examples. The paper also addresses issues related to the influence of outliers in the datasets and possible dependencies between variables that can have an important influence on the results of calibration.
Structural problems due to corrosion, ageing, durability, aggressive environments, materials defects, lack of ductility and unforeseen behaviour under seismic loads may significantly compromise the resistance and safety of bridges. Scheduled maintenance of bridges becomes important to ensure complete serviceability of the road network. Among existing bridge management systems (BMSs), this work is a contribution to the evaluation criteria of bridge condition by means of visual inspection, prediction of future structural condition and planning of maintenance intervention. After a brief review of some existing BMSs, a simple new procedure for evaluation of bridge condition by means of visual inspection, aimed at general planning of maintenance in a BMS framework, is presented in this paper. This procedure is applied to stock, including about 200 bridges and viaducts, of the Veneto region road network in the north-eastern part of Italy, and is then discussed.
Routine bridge inspections enable timely maintenance plans based on potential risks. These inspections are performed at fixed time intervals, which may be unnecessarily short for bridges in excellent conditions and precariously long for those in poor conditions. Also, deterioration models used in current bridge management approaches fail to include the effect of age, causing inaccurate predictions. This paper develops a hazard-based bridge management approach where inspection frequencies and repair suggestions are optimized based on hazard levels. A two-dimensional Markov model describes the age-dependent bridge deterioration, and a semi-Markov decision process optimizes inspection intervals and repair decisions. The objective is to minimize the expected long-term total annual costs, including expenses associated with maintenance and traffic detours. The modeling process and maintenance management framework was demonstrated on an example bridge superstructure in New York. The results showed lower annual and total costs than the conventional management method. As expected, sensitivity analyses indicated that the pre-specified choices of inspection intervals for low and high hazard levels have a significant effect on the calculated hazard threshold levels. The proposed maintenance management method is adaptable and practical; bridge authorities can identify appropriate repair actions and make inspection decisions based on simple threshold comparisons.
In North America, common practices in bridge condition assessment include visual inspection and nondestructive evaluation (NDE) techniques, and results are reported as condition ratings of the bridge components. Assigning a specific condition rating to the component is a difficult task, especially when the threshold values defining the borderlines between the different ratings are not specified. These thresholds are subjectively assigned based on the judgment and experience of the inspector or expert, and can influence decisions on maintenance, repair, and replacement (MRR) of bridges and impact their safety and serviceability. Quality inspection data and accurate condition assessment and rating are the basis for determining appropriate MRR decisions. Thus, in this paper, a novel quality function deployment (QFD)-based approach for assessing bridges is proposed to develop an integrated condition rating based on data collected from visual inspection and ground penetrating radar (GPR) technology, while identifying clear thresholds between the different ratings. The k-means clustering technique, used to define the rating thresholds, is one of the unsupervised learning algorithms that solves the subjective determination of the threshold values problem. This work used four case studies on bridges in the Province of Quebec. The integrated condition model produced ratings of 0.48, 0.49, 0.37, and 0.15 for the four case studies. The developed rating model represented by an integrated condition index was validated with an average validity percentage greater than 81%. The proposed method is expected to advance the state of the art for bridge condition assessment and rating by providing an objective means for making proper MRR decisions.
This paper analyses the correlation between the building quality of masonry bridges and their related ageing process. This is achieved by combining a qualitative evaluation of the construction quality, based on the Masonry Quality Index, with procedures for estimating the ageing process. In an effort to calibrate this relationship, an initial database containing 4040 bridges located in two Spanish regions was filtered with criteria based on materials (only masonry bridges are considered) and the number of recorded inspections. This resulted in a final database containing 22 bridges, which represent the case studies in this research. Next, proper fit methods were applied to the selected structures in order to segment and parametrically study the condition of ageing in relation to the construction quality. Finally, the results of this analysis were used to investigate the ageing process in relation to other factors, properties and fundamental features of the masonry structures. The developed research seeks to: (a) enlarge the existing knowledge on the quality–decay relationship of masonry structures, (b) calibrate methods based on case studies, and (c) define criteria for considering the influence and correlation between both factors.
The level of significance should be chosen with careful consideration of the key factors such as the sample size, power of the test, and expected losses from Type I and II errors. While the conventional levels may still serve as practical benchmarks, they should not be adopted mindlessly and mechanically for every application.
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.
The paper describes an integrated procedure for evaluation of the service-life curve of existing bridges, calibrated with data derived from visual inspections and aimed at assessing the degree of deterioration of their structural/non-structural elements. Visual inspections allowed a total sufficiency rating for the bridges to be calculated, together with data collection, to be used in Bayesian updating of element deterioration curves. The results were combined with information from simulations of potential bridge deterioration scenarios to quantify mean trends and related uncertainties of the remaining service-life curves of existing bridges. The proposed procedure was applied to two bridge stocks and results are critically discussed. The method used may allow public authorities/private managing companies to optimise economic resources allocated to long/medium-term maintenance plans for the most critical structures.
The quality development on the patch repair of reinforced concrete structures by polymer cement mortar is necessary for improvement of their durability. This study shows key points of the repairing work based upon on-the-spot investigations and clarifies impacts of the work conditions on the adhesion-strength of polymer cement mortar. In addition, this study proposes the creation of license system of the repairing work in order to improve technical skills of the field supervisors and to enhance their senses.
For organisations responsible for managing the UK's national infrastructure, particularly those that are tightly regulated, a major challenge today is how to maintain performance of their infrastructure assets within the constraints of both limited budgets and externally set performance targets. This problem becomes more acute when significant members of an asset base approach an end-of life state at the same time. Resolving this problem has often proved difficult as it doesn't readily lend itself to exploration with traditional analytical and forecasting tools. This paper discusses how system dynamics methods and tools have been successfully used to develop a new approach to asset and infrastructure planning which builds simulation models that both give insight into the nature of the asset time bomb threat but, more importantly, enable the development of asset management polices and strategies that will eliminate the threat. The paper includes a description of system dynamics showing the detailed structure of the models developed. A case study of a leading European electricity distribution network operator who used system dynamics to understand and solve the asset time bomb threat is described including the outcome which resulting in a fundamental change in asset management strategy.
The test is based on the maximum difference between an empirical and a hypothetical cumulative distribution. Percentage points are tabled, and a lower bound to the power function is charted. Confidence limits for a cumulative distribution are described. Examples are given. Indications that the test is superior to the chi-square test are cited.
Accumulation of research in the field of bridge evaluation based on structural reliability justifies the consideration of reliability index as the primary measure of safety for bridges. Furthermore, the lifetime bridge evaluation techniques are being mostly based on reliability methods. Currently, bridge safety is measured in terms of the rating factor, which reflects the live load capacity of the structure. In order to investigate the reliability index of an existing bridge and to consider its relation to the rating factor, an in-depth investigation of the interaction between these two safety measures for different limit states of different bridge types is desirable. This paper presents the results of a study in which the interaction between rating and reliability of a group of 14 bridges in an existing bridge network was investigated. This investigation is based on advanced methods to evaluate the reliability of each bridge in the network. Rating factors for different bridge groups are identified based on bridge type, and bridge groups are compared based on mean group rating factor. Bridge rating and reliability are quantified when each bridge starts its service life. The bridge rating factor and the reliability index are evaluated for various limit states belonging to different member types within the bridge network. A correlation study between bridge rating factor and reliability index of different bridge types in an existing network reveals interesting results.
Condition assessment in the Swiss bridge management system (KUBA-MS) is performed on the element level. Five condition states are defined based on visual appearance. In order to forecast the condition states of any given element at any given time a relationship must be established between the element age and its condition state. This relationship, which describes the condition evolution, can be obtained empirically from statistical analysis of pairs of consecutive condition assessments (inspections). Markov chains are used in KUBA-MS to represent condition evolution and the transition probabilities are determined using regression analysis of pairs of inspections. Unfortunately there are almost no inspection data for the worst and second worst condition states. The forecasts made using Markov chains are therefore not always reliable. In this paper an alternative approach is suggested, which takes into consideration the physical phenomena underlying element deterioration. This alternative approach is applied to chloride-induced corrosion of steel reinforcement, by far the most common deterioration mechanism in Switzerland. The chloride-induced corrosion is modeled mathematically and numerical simulations of the condition evolution for different values of model parameters are performed. The simulation results have been mapped to condition states as defined in KUBA-MS and Markov transition matrices have been calibrated to fit simulation results.
The test is based on the maximum difference between an empirical and a hypothetical cumulative distribution. Percentage points are tabled, and a lower bound to the power function is charted. Confidence limits for a cumulative distribution are described. Examples are given. Indications that the test is superior to the chi-square test are cited.
The Pontis bridge management system is the predominant bridge management system employed in the United States. The system employs a network optimization model for preservation, formulated as a Markov decision process with a linear program solution procedure. On each bridge, a set of level-of-service standards determines functional needs, whose benefits are calculated according to a user cost model. A multi-year program simulation generates project alternatives by combining preservation and improvement needs on each bridge. The program is optimized within budget constraints by means of an incremental benefit/cost algorithm. The mathematical formulation of each of these components is presented and discussed. Aspects of system development and data management are outlined, along with the current implementation status. California's experience with the use of Pontis in its funding process is highlighted.
Histograms are among the most important graphical objects in statistical practice, providing a consistent estimate of any continuous density function with very few assumptions. Restricting attention to bins of equal width, the histogram is sometimes presented as a frequency chart or normalized to be a true density. The construction of a histogram may be specified by either its bin width or by the number of bins. Sturges' rule gives a number-of-bins formula. The formula was the first rule given in the literature and is still widely implemented in software today. This article reviews the underlying rationale for the rule and indicates when it is most appropriate to use in practice. Copyright © 2009 John Wiley & Sons, Inc.
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Bridge managers are facing ever-increasing tasks of prioritizing limited budgets to cost-effectively maintain normal functionality of a huge inventory of deteriorating civil infrastructures such as highway bridges over the life cycle. A satisfactory maintenance planning scenario should meet managers' specified requirements for the optimum balance between whole-life costing and structural performance. This article presents a general computational procedure to prioritize on an annual basis maintenance efforts for deteriorating reinforced concrete bridge crossheads over a designated time horizon. Within each year, none or one of the available maintenance types with different performance improvement capabilities could be applied and the time of application for any maintenance intervention is considered to be uniformly distributed within a 1-year time interval. Effects of uncertainties associated with bridge crosshead deterioration processes with and without maintenance interventions are considered by means of Monte Carlo simulation to predict probabilistically structural performance and life-cycle maintenance cost. The resulting combinatorial optimization problem is automated by a multiobjective genetic algorithm. It produces a group of different sequences of annualized maintenance interventions that lead to optimized tradeoff among condition, safety, and life-cycle cost objectives. This enables bridge managers to determine a preferred annual maintenance prioritization solution by comparing different alternatives.
The development of a reliable bridge management system (BMS) constitutes a formidable engineering challenge due to both the multiobjective nature of the task and the large amount of information to be considered in formulating the required automated judgments. This article deals with a procedure for bridge condition assessment by visual inspection developed during the planning and preliminary design of the BMS for the public railway networks in Italy. The main modules adopted in the procedure are: bridge inventory, computer-aided visual inspection, automated defect catalog, and priority-ranking procedure. The probabilistic models used to calibrate the condition evaluation algorithm are discussed. Different levels of deficiency have been individuated for each class of bridge structure belonging to the managed stock. The procedure allows comparison and relative ranking of deficiency conditions across different types of bridge structures. The results of a visual inspection campaign conducted for a set of bridges with different structural characteristics are reported and evaluated within the framework of the developed BMS.