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Aim of this study is to provide information about moisture dependent material behaviour of unstabilised loadbearing earth blocks and mortars. Compressive strength and Young’s modulus were investigated after conditioning in varying relative humidity reaching from 40 % up to 95 %. The material composition and physical properties were investigated to understand the influence of relative humidity onto the mechanical properties. A normalisation of strength and stiffness by the values obtained at 23 ◦C and 50 % relative humidity reveals a linear dependence of compressive strength and Young’s modulus that is regardless of the material composition.
Thus, it is possible to describe the influence of relative humidity onto the load-bearing behaviour of unstabilised earth masonry materials in a generally valid formulation.
block and mortar types is analysed with particular regard to the influence of varying relative humidity. The uniaxial compressive strength and deformation characteristics of unstabilised earth blocks and mortars as well as of unstabilised earth block masonry are studied in detail and compared to conventional masonry to evaluate whether the structural design can be made accordingly. An increase of 30 % points in relative humidity leads to a reduction of the masonry´s compressive strength between 33 % and 35 % whereas the Young´s modulus is reduced by 24–29 %. However, the ratio between the Young´s modulus and the characteristic compressive strength of earth block masonry ranges between E33/fk = 283–583 but is largely independent of the relative humidity. The results show that the mechanical properties of the investigated unstabilised earth block masonry are sufficient for load-bearing structures, yielding a masonry compressive strength between 2.3 MPa and 3.7 MPa throughout the range of moisture contents
investigated. In general, the design concept of conventional masonry can be adapted for unstabilised earth masonry provided that the rather low Young´s modulus as well as the moisture dependence of both, compressive strength and Young´s modulus, are sufficiently taken into
account.
Temperature variation can be a nuisance that perturbs vibration based structural health monitoring (SHM) approaches for civil engineering structures. In this paper, temperature affected vibration data is evaluated within a stochastic damage detection framework, which relies on a null space based residual. Besides two existing temperature rejection approaches – building a reference state from an averaging method or a piecewise method – a new approach is proposed, using model interpolation. In this approach, a general reference model is obtained from data in the reference state at several known reference temperatures. Then, for a particular tested temperature, a local reference model is derived from the general reference model. Thus, a well fitting reference null space for the formulation of a residual is available when new data is tested for damage detection at an arbitrary temperature. Particular attention is paid to the computation of the residual covariance, taking into account the uncertainty related to the null space matrix estimate. This improves the test performance, contrary to prior methods, for local and global damages, resulting in a higher probability of detection (PoD) for the new interpolation approach compared to previous approaches.
The local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function,the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The advantages of the data-driven residual are demonstrated in a numerical study, and the importance of including the covariance of the reference null space estimate is shown, which increases the detection Performance.
Fault detection for linear parameter varying systems under changes in the process noise covariance
(2020)
Detecting changes in the eigenstructure of linear systems is a comprehensively investigated subject. In particular, change detection methods based on hypothesis testing using Gaussian residuals have been developed previously. In such residuals, a reference model is confronted to data from the current system. In this paper, linear output-only systems depending on a varying external physical parameter are considered. These systems are driven by process noise, whose covariance may also vary between measurements. To deal with the varying parameter, an interpolation approach is pursued, where a limited number of reference models – each estimated from data measured in a reference state – are interpolated to approximate an adequate reference model for the current parameter. The problem becomes more complex when the different points of interpolation correspond to different noise conditions. Then conflicts may arise between the detection of changes in the eigenstructure due to a fault and the detection of changes due to different noise conditions. For this case, a new change detection approach is developed based on the interpolation of the eigenstructure at the reference points. The resulting approach is capable of change detection when both the external physical parameter and the process noise conditions are varying. This approach is validated on a numerical simulation of a mechanical system.
The statistical subspace-based damage detection technique has shown promising theoretical and practical results for vibration-based structural health monitoring. It evaluates a subspacebased residual function with efficient hypothesis testing tools, and has the ability of detecting small changes in chosen system parameters. In the residual function, a Hankel matrix of Output covariances estimated from test data is confronted to its left null space associated to a reference model. The hypothesis test takes into account the covariance of the residual for decision making. Ideally, the reference model is assumed to be perfectly known without any uncertainty, which is not a realistic assumption. In practice, the left null space is usually estimated from a reference data set to avoid model errors in the residual computation. Then, the associated uncertainties may be non-negligible, in particular when the available reference data is of limited length. In this paper, it is investigated how the statistical distribution of the residual is affected when the reference null space is estimated. The asymptotic residual distribution is derived, where its refined covariance term considers also the uncertainty related to the reference null space estimate. The associated damage detection test closes a theoretical gap for real-world applications and leads to increased robustness of the method in practice. The importance of including the estimation uncertainty of the reference null space is shown in a numerical study
and on experimental data of a progressively damaged steel frame.
Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies.
The fatigue process of concrete under compressive cyclic loading is still not completely explored. The corresponding damage processes within the material structure are especially not entirely investigated. The application of acoustic measurement methods enables a better insight into the processes of the fatigue in concrete. Normal strength concrete was investigated under compressive cyclic loading with regard to the fatigue process by using acoustic methods in combination with other nondestructive measurement methods. Acoustic emission and ultrasonic signal measurements were applied together with measurements of strains, elastic modulus, and static strength. It was possible to determine the anisotropic character of the fatigue damage caused by uniaxial loading based on the ultrasonic measurements. Furthermore, it was observed that the fatigue damage seems to consist not exclusively of load parallel oriented crack structures. Rather, crack structures perpendicular to the load as well as local compacting are likely components of the fatigue damage. Additionally, the ultrasonic velocity appears to be a good indicator for fatigue damage beside the elastic modulus. It can be concluded that acoustic methods allow an observation of the fatigue process in concrete and a better understanding, especially in combination with further measurement methods.
Automated modal analysis for tracking structural change during construction and operation phases
(2019)
The automated modal analysis (AMA) technique has attracted significant interest over the last few years, because it can track variations in modal parameters and has the potential to detect structural changes. In this paper, an improved density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean the abnormal poles in a stabilization diagram. Moreover, the optimal system model order is also discussed to obtain more stable poles. A numerical Simulation and a full-scale experiment of an arch bridge are carried out to validate the effectiveness of the proposed algorithm. Subsequently, the continuous dynamic monitoring system of the bridge and the proposed algorithm are implemented to track the structural changes during the construction phase. Finally, the artificial neural network (ANN) is used to remove the temperature effect on modal frequencies so that a health index can be constructed under operational conditions.
Reliability analysis of deteriorating structural systems requires the solution of time-variant reliability problems.
In the general case, both the capacity of and the loads on the structure vary with time. This analysis can be approached by approximation through a series of time-invariant reliability problems, which is a potentially effective strategy for cases where direct solutions of the time-variant reliability problem are challenging, e.g. for structural systems with many elements or arbitrary load processes. In this contribution, we thoroughly Review the formulation of the equivalent time-invariant reliability problems and extend this approximation to structures for which inspection and monitoring data is available. Thereafter, we present methods for efficiently evaluating the reliability over time. In particular, we propose the combination of sampling-based methods with a FORM (first-order reliability method) approximation of the series system reliability problem that arises in the computation of the lifetime reliability. The framework and algorithms are demonstrated on a set of numerical examples, which include the computation of the reliability conditional on inspection data.
Managing aging engineering structures requires damage identification, capacity reassessment, and prediction of remaining service life. Data from structural health monitoring (SHM) systems can be utilized to detect and characterize potential damage. However, environmental and operational variations impair the identification of damages from SHM data. Motivated by this, we introduce a Bayesian probabilistic framework for building models and identifying damage in monitored structures subject to environmental variability. The novelty of our work lies (a) in explicitly considering the effect of environmental influences and potential structural damages in the modeling to enable more accurate damage identification and (b) in proposing a methodological workflow for model‐based structural health monitoring that leverages model class selection for model building and damage identification. The framework is applied to a progressively damaged reinforced concrete beam subject to temperature variations in a climate chamber. Based on deflections and inclinations measured during diagnostic load tests of the undamaged structure, the most appropriate modeling approach for describing the temperature‐dependent behavior of the undamaged beam is identified. In the damaged state, damage is characterized based on the identified model parameters. The location and extent of the identified damage are consistent with the cracks observed in the laboratory. A numerical study with synthetic data is used to validate the parameter identification. The known true parameters lie within the 90% highest density intervals of the posterior distributions of the model parameters, suggesting that this approach is reliable for parameter identification. Our results indicate that the proposed framework can answer the question of damage identification under environmental variations. These findings show a way forward in integrating SHM data into the management of infrastructures.
Changes in the measured response of structural systems can be an indication of structural damages. However, such changes can also be caused by the effect of varying environmental conditions. To detect, localize and quantify changes or damages in structural systems subject to varying environmental conditions, physics-based models of the structural systems have to be applied which explicitly account for the influence of ambient conditions on the structural behavior. Data obtained from the structural systems should be used to calibrate the models and update predictions. Bayesian system identification is an effective framework for this task. In this paper, we apply this framework to learn the parameters of two competing structural models of a reinforced concrete beam subject to varying temperatures based on static response data. The models describe the behavior of the beam in the uncracked and cracked condition. The data is collected in a series of load tests in a climate chamber. Bayesian model class selection is then applied to infer the most plausible condition of the beam conditional on the available data.
Impact resistance of reinforced concrete (RC) structures can be significantly improved by strengthening RC members with thin composite layers featuring high damage tolerance. Indeed, to limit the well-known vulnerability of cement-based materials against impact loading, the synergistic effects of short fibres and continuous textile meshes as hybrid reinforcement has been proved to be highly beneficial. This paper addresses the characterisation of novel cement-based hybrid composites through accelerated drop-weight impact tests conducted on rectangular plates at different impact energies. Two distinct matrices are assessed, with particular interest in a newly developed limestone calcined clay cement (LC3)-based formulation. Important parameters quantifying energy dissipation capability, load bearing capacity and damage are cross-checked to compute the ballistic limit and estimate the safety-relevant characteristics of the different composites at hand. Although textiles alone can improve the damage tolerance of fine concrete to some extent, the crack-bridging attitude of short, well-dispersed fibres in hybrid composites imparts a certain ductility to the cement-based matrices, allowing a greater portion of the textile to be activated and significantly reducing the amount of matrix spalling under impact.
Repair is an indispensable part of the maintenance of structures over their lifetimes. Structural grouting is a widely used remediation technique for concrete components, trenches, mine subsidence, dam joints, restoration of masonry structures, and geological stabilizations. A structural grout system should be injectable in narrow spaces and hence include ingredients with finer particles. Ultrafine cements are ideal for these type of demanding grouts due to their superior properties compared to that of the less expensive, but coarser ordinary Portland cement (OPC). Supplementary cementitious materials (SCMs) are often used to replace OPC clinker based binder in order to modify certain properties and to reduce costs. The most commonly used SCMs are fly ash (FA), and ground granulated blast furnace slag (GGBS). For various special applications microsilica (MS), and metakaolin (MK) are also used. Identifying the optimum replacement contents of OPC by SCMs are a challenge during the design of such grouts. The aim of this experimental study is to investigate the effect of the selected SCMs (FA, MS and MK) on the slump flow, time of efflux, viscosity, shrinkage, and compressive and flexural strength of ultrafine cement based grouts with constant water-binder ratio and superplasticizer content. The test program was formulated using Box-Behnken design principles. Maximum percentages of replacement with ultrafine cement was 6% by volume of cement for MS and 16% for FA, and MK. The results suggest that most investigated grouts have the potential to be used for structural applications. The appropriate quadratic models are then formulated through statistical tools and presented as response surfaces. The trends indicate that fly ash improves the rheological properties, whereas microsilica and metakaolin positively affect shrinkage and mechanical properties to some extent. Based on the influence of SCMs and priorities among the properties, Decision Matrix Analysis (DMA) is carried out to select the most suitable ones among the SCMs. The analysis suggests that microsilica and fly ash are more suitable as SCMs than metakaolin without affecting the properties.
Superplasticizer and Shrinkage Reducing Admixture Dosages for Microfine Cement in Grout Systems
(2019)
Grouts have numerous applications including crack repair as maintenance in construction industries. Microfine cements are intensively used for high strength mortar and grout products. They are ideal for injection grouting in structural repair. Such grouts should have suitable rheological properties to be injectable, especially those used in repair and rehabilitation. The use of superplasticizers (SP) in these products is thus becoming increasingly crucial to achieve favorable workability and viscosity properties. A difficulty in such grouts is the plastic shrinkage due to finer particles used. It is thus necessary to determine optimum SP and shrinkage reducing admixture (SRA) dosages for a microfine cement based grout. In this study, a saturation dosage was decided from two Polycarboxylate ether (PCE) based SPs in relation to neat cement using slump flow and rheological parameters. A range of grout mixtures was formulated containing micro silica (MS) and fly ash (FA), and tested for suitable rheological and mechanical parameters. Based on the results, a grout mixture with MS and FA was selected to determine optimum SRA content. According to the results, a SP dosage of 3% by weight of neat cement is sufficient to achieve saturation. The grout material including MS and FA can produce comparable properties to neat cement grout. MS is found to improve compressive strength within the range considered, whereas a higher FA content provides favourable rheological properties. Finally, a SRA dosage of 4%, which could reduce the shrinkage by about 43% after 28d days, is determined for the grout system.
Identifying optimal inspection and repair strategies for offshore jacket structures is a challenging task. We pre-sent an approach, which is based on recent developments in the field of risk-based operation and maintenance planning at the structural system level. The approach utilizes heuristics to define inspection and repair strate-gies at the system level and to reduce the search space of possible strategies. For each defined strategy, the expected service life cost of inspection, repair and failure is evaluated based on simulated inspection and re-pair histories. Subset simulation is applied to compute the conditional repair and failure probabilities required for this analysis. It also forms the basis for simulating inspection and repair histories. The strategy that mini-mizes the expected service life cost is the optimal one in the set of pre-selected strategies. The underlying condition and performance model accounts for the stochastic dependence among the deterioration states of the different structural elements and the structural redundancy. The approach is demonstrated in a case study considering a jacket-type frame. In this study, we essentially vary the inspection interval, the minimum num-ber of inspected components and the target reliability, and identify the combination that minimizes the ex-pected total service life cost.
The Perfectly Matched Layer (PML) method is an efficient approach to imposing radiation conditions at the bounded region of interest in case of wave propagation in unbounded domains. This paper presents and validates 3D FE/PML numerical schemes based on two different PML formulations for homogeneous and inhomogeneous geological media exhibiting discrete or continuous inhomogeneity. In the equation of motion for the PML domain the applied stretching behavior is expressed either as complex material properties or as complex coordinates. Both PML formulations are implemented in the FEM and verified against analytical solutions. Three different types of material inhomogeneity are considered: layered half-space, continuously inhomogeneous half-space with linear velocity profile and continuously inhomogeneous half-space with nonlinear velocity profile. Sensitivity analyses are conducted, and the performance of the developed numerical schemes is investigated taking into account a broad variation of the PML parameters. Recommendations are given for the optimal values of the PML parameters for the case of homogeneous and inhomogeneous geological media.
An assessment of the elastic-plastic buckling limit state for multi-strake wind turbine support towers poses a particular challenge for the modern finite element analyst, who must competently navigate numerous modelling choices related to the tug-of-war between meshing and computational cost, the use of solvers that are robust to highly nonlinear behaviour, the potential for multiple near-simultaneously critical failure locations, the complex issue of imperfection sensitivity and finally the interpretation of the data into a safe and economic design.
This paper reports on an international ‘round-robin’ exercise conducted in 2022 aiming to take stock of the computational shell buckling expertise around the world which attracted 29 submissions. Participants were asked to perform analyses of increasing complexity on a standardised benchmark of an 8-MW multi-strake steel wind turbine support tower segment, from a linear elastic stress analysis to a linear bifurcation analysis to a geometrically and materially nonlinear buckling analysis with imperfections. The results are a showcase of the significant shell buckling expertise now available in both industry and academia.
This paper is the first of a pair. The second paper presents a detailed reference solution to the benchmark, including an illustration of the Eurocode-compliant calibration of two important imperfection forms.
This paper scopes the specialties of a hazard identification study for large and extra-large unmanned undersea vehicles (UUVs). A generic node/function structure is derived from different large and extra-large UUV designs, partially own vehicle designs from research projects, and partially from commercial vehicles. For each node, a short overview of its components and the proper function or operations is defined. A set of guide words is used to prompt a hazard discussion for each node, which identifies unwanted functions, resulting in potential hazards and unmitigated consequences. Related to the SafeMASS-Report from DNV, this work concentrates on selected topics of the bridge-related function on voyage, control & monitoring, and abnormal situations. For unmanned vehicles without any person on board, these three topics become more important for underwater vehicles with very limited connectivity or temporary disruptions of communication whereby an operator in a remote-control center could only be provided with rough vehicle or mission states. Therefore, vehicle control, whether in hard- or software, must handle most tasks belonging to the considered functions automatically or autonomously. Hence, the most appropriate reactions of the system to unwanted system
behavior must be implemented. This HAZID study could, on the one hand, be used as background for such an implementation, and on the other hand, it is used to verify the implemented actions on the risks and hazards.
Cerro do Jarau is a conspicuous, circular morpho‐structural feature in Rio Grande do Sul State (Brazil), with a central elevated core in the otherwise flat “Pampas” terrain typical for the border regions between Brazil and Uruguay. The structure has a diameter of approximately 13.5 km. It is centered at 30o12′S and 56o32′W and was formed on basaltic flows of the Cretaceous Serra Geral Formation, which is part of the Paraná‐Etendeka Large Igneous Province (LIP), and in sandstones of the Botucatu and Guará formations. The structure was first spotted on aerial photographs in the 1960s. Ever since, its origin has been debated, sometimes in terms of an endogenous (igneous) origin, sometimes as the result of an exogenous (meteorite impact) event. In recent years, a number of studies have been conducted in order to investigate its nature and origin. Although the results have indicated a possible impact origin, no conclusive evidence could be produced. The interpretation of an impact origin was mostly based on the morphological characteristics of the structure; geophysical data; as well as the occurrence of different breccia types; extensive deformation/silicification of the rocks within the structure, in particular the sandstones; and also on the widespread occurrence of low‐pressure deformation features, including some planar fractures (PFs). A detailed optical microscopic analysis of samples collected during a number of field campaigns since 2007 resulted in the disclosure of a large number of quartz grains from sandstone and monomict arenite breccia from the central part of the structure with PFs and feather features (FFs), as well as a number of quartz grains exhibiting planar deformation features (PDFs). While most of these latter grains only carry a single set of PDFs, we have observed several with two sets, and one grain with three sets of PDFs. Consequently, we here propose Cerro do Jarau as the seventh confirmed impact structure in Brazil. Cerro do Jarau, together with Vargeão Dome (Santa Catalina state) and Vista Alegre (Paraná State), is one of very few impact structures on Earth formed in basaltic rocks.