Emerging Applications of Reversible Data Hiding

Reversible data hiding (RDH) is one special type of information hiding, by which the host sequence as well as the embedded data can be both restored from the marked sequence without loss. Beside media annotation and integrity authentication, recently some scholars begin to apply RDH in many other fields innovatively. In this paper, we summarize these emerging applications, including steganography, adversarial example, visual transformation, image processing, and give out the general frameworks to make these operations reversible. As far as we are concerned, this is the first paper to summarize the extended applications of RDH.Comment: ICIGP 201

FRAMEWORK FOR THE FULLY PROBABILISTIC ANALYSIS OF EXCAVATION-INDUCED SERVICEABILITY DAMAGE TO BUILDINGS IN SOFT CLAYS

In this dissertation, a framework for a fully-probabilistic analysis of the potential for building serviceability damage induced by an excavation in soft clays is established. This analysis framework is established based on the concept of a serviceability limit state where the resistance is represented by the capacity of a building to resist serviceability damage, and the loading is represented by the demand on a building due to excavation-induced ground movements. In this study, both the resistance and the loading are treated as a random variable; the resistance is characterized empirically based on a database of the observed building performance while the loading is estimated for a specific case using semi-empirical models that were created with the results of finite element analysis and field observations. A simplified procedure is developed for estimating the loading on a building induced by an excavation. In this simplified procedure, the loading is expressed in terms of damage potential index (DPI) that is based on the concept of principal strain. On the other hand, the resistance as a random variable is characterized based on observed building performance, also in terms of the DPI. The uncertainties of both the resistance and the loading are fully characterized in this dissertation study to enable a fully probabilistic analysis. The developed framework for the fully-probabilistic assessment of the potential for excavation-induced building damage is demonstrated with the well-known TNEC case history. Finally, since the observational method is commonly applied to the design and construction of excavation systems, a simplified scheme for updating the soil parameters (and consequently DPI) based on the observations of the maximum wall deflection and ground settlement is developed. This updating scheme is demonstrated with an excavation case history and shown to be an effective technique for monitoring the damage potential of buildings adjacent to an excavation. The developed framework allows for fully-probabilistic assessment of the potential of building damage induced by an excavation, and thusly, provides engineers with a more transparent assessment of the risk associated with a particular excavation design and construction. Furthermore, with the observational method, the potential for excavation-induced serviceability damage can be reassessed as the excavation proceeds. With this approach, the excavation system can be monitored as the excavation proceeds and necessary measures can be taken to prevent damage to buildings adjacent to the excavation

Evaluation of Undrained Shear Strength of Soil, Ultimate Pile Capacity and Pile Set-Up Parameter from Cone Penetration Test (CPT) Using Artificial Neural Network (ANN)

Over the years, numerous design methods were developed to evaluate the undrained shear strength, Su, ultimate pile capacity and pile set-up parameter, A. In recent decades, the emphasis was given to the in-situ cone and piezocone penetration tests (CPT, PCPT) to estimate these parameters since CPT/PCPT has been proven to be fast, reliable and cost-effective soil investigation method. However, because of the paucity of a vivid comprehension of the physical problem, some of the developed methods incorporate correlation assumptions which might compromise the consistent accuracy. In this study, the Artificial Neural Network (ANN) was exerted using CPT data and soil properties to generate a better and unswerving interpretation of Su, ultimate pile capacity and ‘A’ parameter. In this regard, a data set was prepared consisting of CPT/PCPT data as well as relevant soil properties from 70 sites in Louisiana for the evaluation of Su. For ultimate pile capacity, a database of 80 pile load tests was prepared. Lastly, data was collected from 12 instrumented pile load tests for the interpretation of the ‘A’ parameter. Corresponding CPTs along with the soil borings were also collected. Presenting these data to ANN, models were trained through trial and error using different feed-forward network techniques, e.g. Back Propagation method. Different models of ANN were explored with cone sleeve friction, fs, and tip resistance, qt, as well as plasticity index, PI, effective overburden pressure, σ’vo, etc. as input data and were compared to the conventional methods. It was found that the ANN model with qt, fs, and σ’vo as inputs performed satisfactorily and was found to be better than the conventional empirical method of evaluation of Su. On the other hand, ANN models with pile embedment length, pile width, qt, and fs as inputs, outperformed the best-performed direct pile-CPT methods in the interpretation of ultimate pile capacity. Similarly, the ‘A’ parameter predicted by the ANN models (PI, OCR, and Su as inputs) was also in good agreement with the actual one. These findings, hence, fortifies the applicability of ANN for estimating the undrained shear strength, ultimate pile capacity and ‘A’ parameter from CPT data and soil properties

Comparative Deterministic and Probabilistic Modeling in Geotechnics: Applications to Stabilization of Organic Soils, Determination of Unknown Foundations for Bridge Scour, and One-Dimensional Diffusion Processes

This study presents different aspects on the use of deterministic methods including Artificial Neural Networks (ANNs), and linear and nonlinear regression, as well as probabilistic methods including Bayesian inference and Monte Carlo methods to develop reliable solutions for challenging problems in geotechnics. This study addresses the theoretical and computational advantages and limitations of these methods in application to: 1) prediction of the stiffness and strength of stabilized organic soils, 2) determination of unknown foundations for bridges vulnerable to scour, and 3) uncertainty quantification for one-dimensional diffusion processes. ANNs were successfully implemented in this study to develop nonlinear models for the mechanical properties of stabilized organic soils. ANN models were able to learn from the training examples and then generalize the trend to make predictions for the stiffness and strength of stabilized organic soils. A stepwise parameter selection and a sensitivity analysis method were implemented to identify the most relevant factors for the prediction of the stiffness and strength. Also, the variations of the stiffness and strength with respect to each factor were investigated. A deterministic and a probabilistic approach were proposed to evaluate the characteristics of unknown foundations of bridges subjected to scour. The proposed methods were successfully implemented and validated by collecting data for bridges in the Bryan District. ANN models were developed and trained using the database of bridges to predict the foundation type and embedment depth. The probabilistic Bayesian approach generated probability distributions for the foundation and soil characteristics and was able to capture the uncertainty in the predictions. The parametric and numerical uncertainties in the one-dimensional diffusion process were evaluated under varying observation conditions. The inverse problem was solved using Bayesian inference formulated by both the analytical and numerical solutions of the ordinary differential equation of diffusion. The numerical uncertainty was evaluated by comparing the mean and standard deviation of the posterior realizations of the process corresponding to the analytical and numerical solutions of the forward problem. It was shown that higher correlation in the structure of the observations increased both parametric and numerical uncertainties, whereas increasing the number of data dramatically decreased the uncertainties in the diffusion process

ISGSR 2011 - Proceedings of the 3rd International Symposium on Geotechnical Safety and Risk

Scientific standards applicable to publication of BAWProceedings: http://izw.baw.de/publikationen/vzb_dokumente_oeffentlich/0/2020_07_BAW_Scientific_standards_conference_proceedings.pd

An Experimental and Theoretical Study of Pile Foundations Embedded in Sand Soil

This study aimed to examine the load carrying capacity of model instrumented piles embedded in sand soil, and to develop and verify reliable, highly efficient predictive models to fully correlate the non-linear relationship of pile load-settlement behaviour using a new, self-tuning artificial intelligence (AI) approach. In addition, a new methodology has been developed, in which the most effective pile bearing capacity design parameters can be precisely determined. To achieve this, a series of comprehensive experimental pile load tests were carried out on precast concrete piles, steel closed-ended piles and steel open-ended piles, comprised of three slenderness ratios of 12, 17 and 25, using an innovative calibrated testing rig, designed and manufactured at Liverpool John Moores University. The model piles were tested in a large pile testing chamber at a range of different densities of sand; loose (18%), medium (51%) and dense (83%). It is worth noting that novel structural fibres were utilised and optimised for different volume fractions to enhance the mechanical performance of concrete piles. The obtained results revealed that the higher the values of the of the pile effective length, Lc (embedded length of pile), sand density, and the soil-pile angle of shearing resistance, the higher the axial load magnitudes to reach the yield limit. This can be attributed to the increase in the end bearing point and mobilised shaft resistance. In addition, the plastic mechanism occurring in the surrounding soil was identified as the leading cause for the presence of nonlinearity in the pile-load tests. Furthermore, a new enhanced self-tuning supervised Levenberg-Marquardt (LM) training algorithm, based on a MATLAB environment, was introduced and applied in this process. The proposed algorithm was trained after conducting a comprehensive statistical analysis, the key objectives being to identify and yield reliable information from the most effective input parameters, highlight the relative importance “Beta values” and the statistical significance “Sig values” of each model input variable (IV) on the model output. To assess the accuracy and the efficiency of the employed algorithm, different measuring performance indicators (MPI), suggested in the open literature, were utilised. Common statistical performance indexes, i.e., root mean square error (RMSE), Pearson’s moment correlation coefficient (p), coefficient of determination (R), and mean square error (MSE) for each model were determined. Based on the graphical and numerical comparisons between the experimental and predicted load-settlement values, the results revealed that the optimum models of the LM training algorithm fully characterised load-settlement response with remarkable agreement. Additionally, the proposed algorithm successfully outperformed the conventional approaches, demonstrating the feasibility of the current study. New design charts have been developed to calculate the individual contribution of the most significant pile bearing capacity design parameters “the earth pressure coefficient (K) and the bearing capacity factor (N )”. The improved approach takes into account the change in sand relative density, pile material type, and the pile slenderness ratios. It is therefore a significant improvement over most conventional design methods recommended in the existing design procedures, which do not consider the influence of the most significant parameters that govern the pile bearing capacity design process

Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.

The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome. This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd

Update the Pile Design by CPT Software to Incorporate Newly Developed Pile-CPT Methods and Other Design Features

Code LTRC Project Number: 17-2GT SIO Number: DOTLT1000165This study presents the performance evaluation of 21 direct pile-CPT methods for estimating the ultimate load carrying capacity of square precast prestressed concrete (PPC) piles driven into Louisiana soils utilizing the cone penetration test (CPT) data. The investigated methods are: Schmertmann, De Ruiter and Beringen, Bustamante and Gianeselli (LCPC), Philipponnat, Price and Wardle, Zhou, Tumay and Fakhroo, UF (2007), probabilistic, Aoki and De Alencar, Penpile, NGI, ICP, UWA, CPT2000, Fugro, Purdue, German, Eurocode7, ERTC3, and Togliani direct pile-CPT methods. A search was conducted in the DOTD files to identify pile load test reports with CPT soundings adjacent to test piles. A database of 80 pile load tests that were loaded to failure, were identified, collected, and used in analysis. The measured ultimate load carrying capacity for each pile was interpreted from the pile load test using the Davisson and modified Davisson interpretation methods. The ultimate pile capacities estimated from the pile-CPT methods were compared with the measured ultimate pile capacities. In this study, three approaches were adopted to evaluate the performance of pile-CPT methods. In the first approach, three statistical criteria were used: the best fit line of predicted (Qp) versus measured (Qm) capacity, arithmetic mean and standard deviation of Qp\u2044Qm, and the cumulative probability of Qp\u2044Qm. The results of this evaluation showed the following best-performed pile-CPT methods in order: LCPC, ERTC3, Probabilistic, UF, Philipponnat, De Ruiter and Beringen, CPT2000, UWA, and Schmertmann methods. The second approach used to evaluate the 21 pile-CPT methods is the MultiDimensional Unfolding (MDU), which showed similar ranking of top-performed pile-CPT methods. The third approach used for evaluating the pile-CPT methods was based on LRFD reliability analysis in terms of resistance factor and efficiency, and the results of evaluation are consistent with the previous two criteria