Practical concepts for the use of probabilistic methods in the structural analysis and reassessment of existing bridges - presentation of latest research and implementation

In structural engineering and bridge construction, probabilistic calculations are only performed in a few exceptional cases. The ability to directly use information from the existing structure is an important advantage of probabilistic methods. Geometric data or the position and quantity of the installed reinforcing steel and tendons can be measured. Results from monitoring of the traffic can be used to generate structure-specific load models. Many clients are still very sceptical about the results of probabilistic calculations, even though there are several examples of successful application. The use of probabilistic verification formats is explicitly permitted by the German guideline for the assessment of existing bridges (German: Nachrechnungsrichtlinie). Since a high degree of special knowledge is required for the application of probabilistic calculations, it is difficult to establish the potential of this verification method in practice. It is necessary to enable the practical engineer to utilize the advantages of probabilistic calculations and to include the actual structural conditions into the calculation model, but without the need for all the special knowledge. Structure-specific partial safety factors include measured data and can be used for the well-known and in the codes established semi-probabilistic design concept. The authors present examples for the successful application of probabilistic methods and of data measurement during the reassessment of two existing bridges in Germany. The capabilities of full-probabilistic calculations for the reassessment of existing structures are described and concepts for the calculation of structure-specific partial safety factors are shown

dacl10k: Benchmark for Semantic Bridge Damage Segmentation

Reliably identifying reinforced concrete defects (RCDs)plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide. Nevertheless, available datasets for the recognition of RCDs are small in terms of size and class variety, which questions their usability in real-world scenarios and their role as a benchmark. Our contribution to this problem is "dacl10k", an exceptionally diverse RCD dataset for multi-label semantic segmentation comprising 9,920 images deriving from real-world bridge inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge components that play a key role in the building assessment and recommending actions, such as restoration works, traffic load limitations or bridge closures. In addition, we examine baseline models for dacl10k which are subsequently evaluated. The best model achieves a mean intersection-over-union of 0.42 on the test set. dacl10k, along with our baselines, will be openly accessible to researchers and practitioners, representing the currently biggest dataset regarding number of images and class diversity for semantic segmentation in the bridge inspection domain.Comment: 23 pages, 6 figure

On the treatment of measurement uncertainty in stochastic modeling of basic variables

The acquisition and appropriate processing of relevant information about the considered system remains a major challenge in assessment of existing structures. Both the values and the validity of computed results such as failure probabilities essentially depend on the quantity and quality of the incorporated knowledge. One source of information are onsite measurements of structural or material characteristics to be modeled as basic variables in reliability assessment. The explicit use of (quantitative) measurement results in assessment requires the quantification of the quality of the measured information, i.e., the uncertainty associated with the information acquisition and processing. This uncertainty can be referred to as measurement uncertainty. Another crucial aspect is to ensure the comparability of the measurement results.This contribution attempts to outline the necessity and the advantages of measurement uncertainty calculations in modeling of measurement data-based random variables to be included in reliability assessment. It is shown, how measured data representing time-invariant characteristics, in this case non-destructively measured inner geometrical dimensions, can be transferred into measurement results that are both comparable and quality-evaluated. The calculations are based on the rules provided in the guide to the expression of uncertainty in measurement (GUM). The GUM-framework is internationally accepted in metrology and can serve as starting point for the appropriate processing of measured data to be used in assessment. In conclusion, the effects of incorporating the non-destructively measured data into reliability analysis are presented using a prestressed concrete bridge as case-study

Uncertainty assessment for the Bayesian updating process of concrete strength properties

Reassessment of infrastructure buildings has become an essential approach to deal with increasing traffic loads on ageing infrastructure buildings and to verify the service-life of those structures. Good estimation of the actual material properties is highly relevant for reliable structural reassessment. Although this holds for all building materials, the importance of good parameter estimation is of special importance for concrete structures, where the strength properties show relatively high variation and where the nominal strength properties tend to be too conservative. Modern design guidelines allow to make use of scientific methods such as Bayesian Updating of material properties to enable a more realistic consideration of the actual material properties in the reassessment of existing structures. However, guidelines for application and experience with those methods are not yet reported much or are rather vague [1]. The presented study focuses on the effect of the Bayesian Updating process for material parameters with special emphasis on the number and sampling location of test specimens as well as on the accuracy and confidence in the obtained posterior distribution, since sampling also includes a certain margin of uncertainty. The investigation on the methodological potential and on the uncertainty margin in the updating process in this contribution uses a batch of 14 test results on the concrete compressive strength obtained from drill cores along with the inherent measurement uncertainties from the testing procedure. After a short review of Bayes’ Theorem, the Markov Chain Monte Carlo Method (MCMC) and the bootstrap methodology, all combinations of subsamples of size 1, 3 and 5 specimens were built and provided to the Bayes’ updating procedure via MCMC to determine the posterior distributions. The series of obtained posterior distributions for a certain subsample was used to determine the uncertainty in the Bayesian Updating process by evaluation of the scatter in the expected value, the standard deviation and the 5 %-quantile of the updated distribution. The simulations show the importance of an adequate sample size and quantify the uncertainties arising from the limited number of observations

Use of Steel Fiber Reinforced Concrete for the Protection of Buildings Against High Dynamic Actions

Nowadays ensuring the safety of people and the protection of infrastructure is a socially relevant topic, which requires a thorough investigation. The Institute of concrete construction at the University of the German Armed Forces in Munich is investigating the possibilities of using steel fiber concrete for the protection of military facilities and state-owned special buildings in Germany. In this research project steel fiber reinforced concrete is investigated under high dynamic loads specifically under contact detonation loading. Plates with varying reinforcement systems, different thicknesses, different fiber geometries, fiber contents and fiber types were produced.The following concrete compressive strengths C20/25, C40/50, C80/95 were used in this research project.The plates were loaded with 500 g, 750 g, 1000 g, 1500 g and 2000 g PETN explosive at the test facility of the German Armed Forces Technical centre for Protective and Special Technologies. An important property of construction material during ballistic threats and contact detonation is the concrete tensile strength. Through the addition of fibers, the post cracking behavior and the ductility of concrete components can be improved. All fiber-reinforced specimens showed less damage than the non-fiber reinforced elements. The aim of the study is to optimize the concrete mixture for the fiber concrete protection components considering the following factors: concrete quality, fiber content, fiber geometry, as well as aggregate size of the concrete. Another aim is to record and evaluate the damage parameters of the steel fiber reinforced concrete slabs after the highly dynamic load, and to investigate to offer suggestion for retrofitting. In this article, the test results of different steel framed concretes under highly dynamic conditions are presented

Retaining foundation for traffic sign bridges

Gemäß Eurocode 1 ist für die Unterbauten von Brücken außerorts eine horizontale Anpralllast von 1500 kN zu berücksichtigen. Gemäß der ZTV-ING ist für die Stiele von Schilderbrücken eine Anpralllast von 100 kN anzusetzen. Im Rahmen des Forschungsvorhabens wurde aufbauend auf einer qualitativen und quantitativen Risikoanalyse sowie der Berechnung der Auftretenswahrscheinlichkeit eine statische Ersatzkraft für ein Anprallereignis anhand dynamischer numerischer FE-Berechnungen berechnet. Die Auftretenswahrscheinlichkeit für P(Anprall mit Todesfolge / km) konnte auf der Grundlage recherchierter Anprallereignisse mit 6,8 x 10-5 / Jahr berechnet werden. Im Rahmen einer qualitativen Risikoanalyse konnte festgestellt werden, dass die Anordnung von Fahrzeugrückhaltesystemen eine deutliche Senkung des Gefährdungsgrades für den Anprallenden und für Dritte bewirkt. Am Gesamtmodell einer Verkehrszeichenbrücke wurden dynamische Berechnungen mit dem Programm Siemens NX durchgeführt. Der Anprallkörper wurde dabei so modelliert, dass der LKW Anprall möglichst realistisch nachempfunden werden kann. Es zeigte sich, dass ein Anprallsockel gemäß Richtzeichnung Riz-Ing VZB 4 eine deutliche Reduzierung des Anprallereignisses in Hinblick auf Geschwindigkeit und einwirkende Masse an die Stütze bewirkt. Die statischen und dynamischen Berechnungen haben zudem gezeigt, dass durch die Berücksichtigung der dynamischen Effekte eine Steigerung der Tragfähigkeit von ca. 90 % durch die Trägheit der Verkehrszeichenbrücke und ca. 50% durch die Dehnrateneffekte zu erwarten ist. Es wird empfohlen die statische Ersatzlast mit H = 100 kN zu berücksichtigen. Der Angriffspunkt der statischen Ersatzlast liegt dabei 1,25 m über OK Fahrbahn. Für die Erhöhung der Querkrafttragfähigkeit und der lokalen Tragfähigkeit der Stütze am Anprallort wird die Anordnung von 2 zusätzlichen Steifen vorgeschlagen. Die beim Aufprall auftretenden Kontaktkräfte zwischen Fahrzeug und Verkehrszeichenbrücke, die um ein Vielfaches höher sind, können mit der so bemessenen Konstruktion mit großen plastischen Verformungen aufgenommen werden, sofern ein Anprallsockel vorhanden ist.According to Eurocode 1 a horizontal impact load of 1500 kN should be taken into account at the substructures of the bridges. In addition, an impact load of 100 kN should be set at the columns of the traffic sign bridges, according to ZTV-ING. Under the framework of the research project, a qualitative and quantitative risk analysis was executed, as well as the calculation of the probability of occurrence and the calculation of a static impact load for an impact event, by the use of dynamic numerical FE-calculations. The probability of occurence for a impact event P (impact fatalities/km) was calculated based on the researched data with 6.8 x 10-5 / year. Under the framework of a qualitative risk analysis, it was concluded that the arrangement of road restraint systems plays a significant role for the reduction of the degree of danger for the crashed vehicle, its occupants and the third parties. By the use of Siemens NX program, dynamic calculations were executed at the overall model of a traffic sign bridge. The impact load was modelled as realistic as possible. It showed that a retaining foundation, according to german standard drawing Riz-Ing VZB 4, lead to a significant reduction of the impact event in terms of speed and acting mass to the support. The static and dynamic calculations showed that taking into account the dynamic effects, an increase of the structural resistance of about 90% is expected, due to the inertia characteristic of the traffic sign bridge and about 50% due to the expansion rate effects. It is recommended the static impact load of H = 100 kN to be considered. The point of application of the static equivalent load is 1.25 m above the OK roadway. The integration of 2 additional steel plates is proposed, in order to increase the shear capacity and the local load capacity of the support at the impact location. The contact forces between the vehicle and the traffic sign bridge, which are higher by a multiple, can be taken up with the construction thus dimensioned with large plastic deformations, provided that a retaining foundation is present

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods

Uncertainty assessment for the Bayesian updating process of concrete strength properties

Reassessment of infrastructure buildings has become an essential approach to deal with increasing traffic loads on ageing infrastructure buildings and to verify the service-life of those structures. Good estimation of the actual material properties is highly relevant for reliable structural reassessment. Although this holds for all building materials, the importance of good parameter estimation is of special importance for concrete structures, where the strength properties show relatively high variation and where the nominal strength properties tend to be too conservative. Modern design guidelines allow to make use of scientific methods such as Bayesian Updating of material properties to enable a more realistic consideration of the actual material properties in the reassessment of existing structures. However, guidelines for application and experience with those methods are not yet reported much or are rather vague [1]. The presented study focuses on the effect of the Bayesian Updating process for material parameters with special emphasis on the number and sampling location of test specimens as well as on the accuracy and confidence in the obtained posterior distribution, since sampling also includes a certain margin of uncertainty. The investigation on the methodological potential and on the uncertainty margin in the updating process in this contribution uses a batch of 14 test results on the concrete compressive strength obtained from drill cores along with the inherent measurement uncertainties from the testing procedure. After a short review of Bayes’ Theorem, the Markov Chain Monte Carlo Method (MCMC) and the bootstrap methodology, all combinations of subsamples of size 1, 3 and 5 specimens were built and provided to the Bayes’ updating procedure via MCMC to determine the posterior distributions. The series of obtained posterior distributions for a certain subsample was used to determine the uncertainty in the Bayesian Updating process by evaluation of the scatter in the expected value, the standard deviation and the 5 %-quantile of the updated distribution. The simulations show the importance of an adequate sample size and quantify the uncertainties arising from the limited number of observations.ISSN:2336-538

Reliability assessment of existing bridge constructions based on results of non-destructive testing

The non-destructive testing methods available for civil engineering (NDT-CE) enable the measurements of quantitative parameters, which realistically describe the characteristics of existing buildings. In the past, methods for quality evaluation and concepts for validation expanded into NDT-CE to improve the objectivity of measured data. Thereby, a metrological foundation was developed to collect statistically sound and structurally relevant information about the inner construction of structures without destructive interventions. More recently, the demand for recalculations of structural safety was identified. This paper summarizes a basic research study on structural analyses of bridges in combination with NDT. The aim is to use measurement data of nondestructive testing methods as stochastic quantities in static calculations. Therefore, a methodical interface between the guide to the expression of uncertainty in measurement and probabilistic approximation procedures (e.g. FORM) has been proven to be suitable. The motivation is to relate the scientific approach of the structural analysis with real information coming from existing structures and not with those found in the literature. A case study about the probabilistic bending proof of a reinforced concrete bridge with statistically verified data from ultrasonic measurements shows that the measuring results fulfil the requirements concerning precision, trueness, objectivity and reliability