COMPLAS 2021 is the 16th conference of the COMPLAS Series.
The COMPLAS conferences started in 1987 and since then have become established events in the field of computational plasticity and related topics. The first fifteen conferences in the COMPLAS series were all held in the city of Barcelona (Spain) and were very successful from the scientific, engineering and social points of view. We intend to make the 16th edition of the conferenceanother successful edition of the COMPLAS meetings.
The objectives of COMPLAS 2021 are to address both the theoretical bases for the solution of nonlinear solid mechanics problems, involving plasticity and other material nonlinearities, and the numerical algorithms necessary for efficient and robust computer implementation. COMPLAS 2021 aims to act as a forum for practitioners in the nonlinear structural mechanics field to discuss recent advances and identify future research directions.
Scope
COMPLAS 2021 is the 16th conference of the COMPLAS Series.
Laboratory and geophysical tests are commonly used in site characterization. Combining these data sets based on empirical relationships can essentially enhance data interpretation. While in traditional approaches, the uncertainties in the relationship between these data sets are ignored. The Bayesian updating method is used to consider these uncertainties. Besides, the uncertainties due to measurement errors in the laboratory tests, particularly for preconsolidation pressure, are considered based on the kriging fitting method. The outcomes of kriging fitting are utilized to establish the prior distribution, and these outcomes are then compared against the baseline established by the trend fitting method. The Markov chain Monte Carlo (MCMC) algorithm is applied to incorporate the shear wave velocity measurements from a seismic dilatometer test to derive the posterior distribution. Bayesian updating of parameters considering measurement errors is able to get a more convincing design profile.
Abstract Laboratory and geophysical tests are commonly used in site characterization. Combining these data sets based on empirical relationships can essentially enhance data interpretation. [...]
The preservation and documentation of cultural heritage sites are fundamental to safeguarding our shared history and identity. This study explores the innovative application of the viDoc RTK rover for cultural heritage documentation, presenting a forward-looking approach to capturing high-precision spatial data in the preservation and analysis of Brari bridge located in Tirana, Albania. Handheld mobile terrestrial laser scanning (HMTLS) offers versatility and mobility, enabling rapid and non-invasive data acquisition in complex and challenging environments. The methodology encompasses equipment selection, data acquisition techniques, and data processing workflows tailored for HMTLS technology. The results demonstrate the potential of HMTLS to produce highly accurate 3D models, showcasing intricate architectural details and capturing fine surface textures with an accuracy of 3 cm. Furthermore, the portability of handheld devices allows for documentation in areas where traditional scanning methods may be impractical. This study underscores the transformative impact of HMTLS on cultural heritage preservation, offering a cost-effective, efficient, and accessible means of creating digital archives. The adoption of this technology contributes to the long-term conservation, research, and education associated with our cultural heritage, ensuring that these invaluable assets continue to inspire and inform future generations.
Abstract The preservation and documentation of cultural heritage sites are fundamental to safeguarding our shared history and identity. This study explores the innovative application [...]
The success of numerical analysis relies on several factors, with one crucial aspect being the accurate determination of constitutive model parameters. Extracting these parameters directly from in-situ tests has several advantages, such as costeffectiveness and minimal soil disturbance. However, obtaining soil parameters directly from in-situ tests is not feasible, as empirical correlations are used to interpret them. An ongoing research project aims to create an automated parameter determination (APD) framework using a graph-based approach to determine constitutive model parameters from in-situ tests. The process involves using two spreadsheets as input: the first defines the parameters, while the second specifies the correlations used to compute them. The system then generates connections between the parameters and computes values for each one. The paper discusses the validation of the correlations database used by the system, which includes over 100 correlations for deriving parameters for various soil types. The framework determines parameters based on cone penetration tests (CPT), dilatometer tests (DMT), and in-situ shear wave velocity measurements. The system's output is compared to values interpreted from laboratory tests. To collect data for this validation, a web-based application "Datamap" was employed, which stores and categorizes geotechnical data. The validation process utilized data from the Norwegian GeoTest Sites (NGTS), specifically the NGTS-silt project. The parameters were calculated based on CPT, DMT, and in-situ shear wave velocity measurements. Ongoing research aims to evaluate the accuracy of the derived parameters and expand the system's capabilities to include additional in-situ tests
Abstract The success of numerical analysis relies on several factors, with one crucial aspect being the accurate determination of constitutive model parameters. Extracting these parameters [...]
The distribution of natural strata is uncertain due to tectonic movements and sedimentation. Capturing geological uncertainty is a challenge for traditional deterministic models. In this study, an improved three-dimensional coupled Markov chains method for probabilistic stratigraphic reconstruction was developed. This method considers the correlation between the field borehole data. On this basis, an inversion analysis method for horizontal transition probability matrix estimation is proposed. This method makes the predictions more suitable for possible stratigraphic distributions. The accuracy of the method was further verified by different borehole schemes from the Mawan Tunnel in Shenzhen. The results show that the proposed method can still have high accuracy when the number of boreholes is sparse. This method can reflect the asymmetry, continuity and anisotropy of three-dimensional strata.
Abstract The distribution of natural strata is uncertain due to tectonic movements and sedimentation. Capturing geological uncertainty is a challenge for traditional deterministic [...]
In engineering geology and geotechnical engineering, subsurface soils and rocks are natural geomaterials and exhibit inherent variability in stratigraphy due to geological deposition process. Explicit knowledge of subsurface stratigraphy is a critical input for the analysis, design, and construction of geotechnical engineering systems. However, the accurate and reliable modelling of subsurface geological stratigraphy is challenging due to the limited number of available boreholes in practice and the complex nature of soil stratigraphy. This paper presents an innovative machine learning framework built upon the neighborhood aggregation technique for the prediction of digitized subsurface geological stratigraphy. To predict the stratigraphy at a given point of interest, neighborhood aggregation is first performed to intelligently consolidate the stratigraphy information from its neighboring boreholes, resulting in additional features associated with the target location. By combining the extra stratigraphy information with conventional location-specific features, the framework enhances the predictive capabilities of classical machine learning models at a finer scale. The proposed framework is implemented using common machine learning models and is validated using a simulated benchmark 3D example. The results of leave-one-out cross-validation demonstrate that the proposed framework can improve the performance of classical machine learning models, leading to more reasonable stratigraphy transition and associated uncertainty quantification.
Abstract In engineering geology and geotechnical engineering, subsurface soils and rocks are natural geomaterials and exhibit inherent variability in stratigraphy due to geological [...]
In cone penetration testing (CPT) an electronic penetrometer is pushed at a constant rate into penetrable soils and cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) are recorded with depth. The measured qc, fs and u values are utilized to estimate soil type and associated properties. Cone tips have areas which vary from 5cm2 to 40 cm2. The larger tips allow for the penetration of gravely soils while small cone tips are utilized for shallow soil investigations. The measured cone bearing and sleeve friction values are blurred or averaged. The measurements are also susceptible to anomalous peaks and troughs due to the relatively small diameter cone tip penetrating sandy, silty and gravelly soils. The cones with relatively smaller cone tips are significantly more susceptible to the anomalous peaks and troughs while the cones with larger cone tips are more susceptible to the smoothing of the cone tip and sleeve friction measurements. Baziw Consulting Engineers (BCE) has invested considerable resources in addressing the qc and fs measurements distortions. This paper outlines the techniques developed by BCE and integrates them so that optimal soil properties can be obtained from CPT data sets. Particular focus is put on relatively larger cone tips because they can penetrate soils with high resistance and are less susceptible to the additive measurement noise of anomalous peaks and troughs. The anomalous peaks and troughs are more challenging to remove or minimize than the qc and fs blurring effects. It is of paramount importance to first implement newly developed signal processing and optimal estimation algorithms on extensive test bed simulations prior to processing real data sets. This paper also outlines the results from processing a challenging test bed simulation of a 40 cm2 cone tip data set with BCE’s newly developed algorithms.
Abstract In cone penetration testing (CPT) an electronic penetrometer is pushed at a constant rate into penetrable soils and cone bearing (qc), sleeve friction (fc) and dynamic pore [...]
In critical state soil mechanics, the critical state refers to the combination of effective stress and void ratio (e) at which a soil continues to shear with no change in effective stress, shear stress, and e. The phenomena can be visualized using the critical state line (CSL). The CSL represents the locus of e at critical state with effective mean stress (σ′mean). To define the CSL, the CSL slope (λ), termed “compressibility,” and CSL y-axis intercept at 1 kPa (Γ), termed “altitude,” are required. The CSL in e – σ′mean space provides a simple model of complex soil behavior that allows engineers to construct constitutive models using the state parameter (ψ), which is the mathematical difference between the in-situ e of the soil and the e of the soil at critical state. Currently, Γ can be obtained only through laboratory testing, while λ and ψ can be obtained via laboratory testing or correlation. This paper presents forthcoming correlations based on the ΔQ soil behavior index (which is obtained via the cone penetration test, CPT) to forecast Γ, λ, and ψ, and compares the ΔQ-based correlations’ performance to other CPT-based correlations as well as to data obtained from literature. To compare the correlations, the authors used data from a site investigation performed in Fraser River sand as part of the Canadian Liquefaction Experiment.
Abstract In critical state soil mechanics, the critical state refers to the combination of effective stress and void ratio (e) at which a soil continues to shear with no change in [...]
Soil boundary delineation is an important task in geotechnical site characterization. It can be achieved by either extracting borehole samples, conducting laboratory tests, and classifying them according to a soil classification system such as the Unified Soil Classification System (USCS) or utilizing multiple cone penetration test (CPT) soundings, and identifying soil boundaries at the soundings from the Ic (soil behavior type index) profiles. However, most soil-layer delineation methods can only take a single type of test result as the input. For instance, the well-known Markov random field (MRF) method can only take soil-type data such as sand, silt, or clay at boreholes as the input. Recognizing that soil classifications and soil properties are correlated, this paper proposes a novel coupled MRF-Bayesian framework to infer the spatial variation of USCS classifications (e.g., sand, silt, and clay) as well as soil properties by integrating both CPT and borehole data. This integrated approach leverages both CPT and borehole data to address some main challenges e.g., uncertainties and multivariate soil data input in underground stratification problems by simultaneous sampling of soil properties and soil types. The new unified framework can accommodate multivariate data, hence the new framework is compatible with the geotechnical engineering practice. The uncertainties for the spatial variation of USCS classification at sounding locations are quantified through a “layer-specific” Bayesian updating i.e., updating posterior cross-correlation behaviors for different layers (such as sand, silt, and clay), independently. In this Bayesian updating, soil-type data can provide some information about the soil properties according to the unified soil classification system. Further, the soil boundaries can be identified across the entire domain by the realization of conditional random fields of soil properties once the spatial variation of USCS classification is inferred at sounding locations, followed by a 3-dimensional Markov random field process.
Abstract Soil boundary delineation is an important task in geotechnical site characterization. It can be achieved by either extracting borehole samples, conducting laboratory tests, [...]
Non-invasive site characterization techniques have the potential to rapidly evaluate large subsurface volumes to guide subsequent invasive geotechnical site investigation. Among these methods, seismic full waveform inversion (FWI) stands out for its potential to recover detailed two-dimensional (2D) images of the subsurface. However, FWI’s need for substantial computational resources and sensitivity to the initial starting model has limited its utilization as a generalpurpose geotechnical site characterization tool. Addressing this, prior studies have shown data-driven methods can predict 2D subsurface structures composed of soil over rock. In the present study, we aim to generalize these findings to all nearsurface conditions. We propose a novel model generation framework that utilizes techniques from geostatistics to generate complex 2D subsurface models. The generated models include dipping soil and rock layers, soil lenses, boulders, and underground utilities; none of which have been considered previously. We use our model generation framework to simulate 100,000 2D subsurface models. We simulate field data acquisition along these 100,000 synthetic models, by numerically solving the elastic wave equation using an impulse source at the model’s center surrounded by 24 receivers (12 on either side). The data-driven predictive model, trained on 90% of the simulated data, achieved a mean absolute percent error on the testing set of 19%. Furthermore, these predictions are made within fractions of a second circumventing the computational and starting-model-related challenges associated with traditional 2D FWI. These results demonstrate that data-driven methods can predict complex images of the subsurface to enable rapid subsurface imaging for geotechnical applications.
Abstract Non-invasive site characterization techniques have the potential to rapidly evaluate large subsurface volumes to guide subsequent invasive geotechnical site investigation. [...]
Porto Romano port complex new facilities are part of the expansion plans of Durres port and its modernisation. However, the relocation site presented many significant spatial and geotechnical challenges to be considered and mitigated. Soil conditions and the country’s high seismic activity meant the project required extensive feasibility and technical studies to find a safe and sustainable approach. For a detailed geological and geotechnical investigation of the area, various geotechnical, geophysical tests were carried out such as: borings, SPT test, CPTU test, Seismic refraction, MASW, Downhole and HVSR allowing to obtain the fundamental resonance frequency of the ground. Field recordings per each layer were than compared and calibrated to the results and tests performed in the laboratory. The construction site displayed a variety of soils from soft to firm silty Clays, to loose to medium dense silty Sands and layers with high organic content. During execution of SPT tests, sandy layers gave more satisfactory results, meanwhile, for silty CLAY layers, the results of CPTU testing were considered in analysis. This detailed soil investigation and characterization served to properly design the new port facilities, identify, and protect from the liquefaction phenomena at this specific site.
Abstract Porto Romano port complex new facilities are part of the expansion plans of Durres port and its modernisation. However, the relocation site presented many significant spatial [...]