Back Analysis Research Articles

Creep evaluation for fiber-reinforced composites with hybrid fibers: From quantifying fiber anchorage effect to homogenization model

A general micromechanical creep model for fiber-reinforced composites with multi-type hybrid fibers is proposed from quantifying the fiber anchorage effects. An average Eshelby tensor-based homogenization method is employed to calculate the effective creep of composites with realistic-shaped (straight, hooked, and wavy) fibers. A fiber anchorage zone is introduced to depict the anchorage effects of fibers and its parameters are obtained by back analysis. The micromechanical model and fiber anchorage zones are validated by comparing with extensive creep experimental data and finite element simulation. According to the micromechanical model, the effects of fiber shapes, types, direction, anchorage zone, and fiber doping on the creep of composites are systematically explored. The results show fiber shapes and types have obvious effects on composites creep. Fiber deflection angle leads to a monotonous increase in creep. The creep with fiber doping is between the two cases without fiber doping. It indicates that the creep of fiber-reinforced composites can be tailored via proper design for these factors.

A novel back analysis framework for the probabilistic risk assessment of subaerial landslide-induced tsunami hazard

A novel back analysis framework for the probabilistic risk assessment of subaerial landslide-induced tsunami hazard

Debris flow simulations for hazard, vulnerability and risk assessment in the Karakorum mountain ranges, northern Pakistan

The Karakorum mountainous ranges in northern Pakistan have frequently witnessed devastating debris flows with damaging impacts on surrounding communities and infrastructure. Debris flow runout modeling and hazard assessment are crucial to develop and implement effective mitigation and adaptation measures. In this study, debris flow runout modeling using multiple scenarios of slope failures is carried out for hazard and risk assessments in the Karimabad watershed of the Hunza Valley in northern Pakistan. A 3D numerical simulation tool RAMMS was used to estimate the hazard intensity parameters (runout distance, flow velocity and height) along the debris flow track, based on the Voellmy model. To calibrate the model inputs, a back analysis of the Haiderabad stream debris flow event of July 26, 2022 has been conducted based on the estimated initial volume and the known runout distance. The calibrated values of the dry friction coefficient μ = 0.07 and turbulent coefficient ξ = 550 m/s2 were utilized for the three potential release areas. Different initial volumes were considered, and the results were combined to determine the debris flow runout distance and other intensity parameters for each scenario. The failure of three selected release areas could potentially block or divert the river Hunza. The integration of RAMMS parameters mainly flow velocity and height into hazard mapping assists in the demarcation of the area and elements at risk which are exposed to the potential impacts of debris flows. The derived debris flow hazard maps show that the road network including the KKH is exposed to debris flows. The impact of the debris flows on the surrounding communities and infrastructure is carried out through the vulnerability assessment and combined with the hazard assessment to derive the risk. Due to the intensive incision in the channels as the path for debris flow, the settlements and populations are less vulnerable to debris flow, however, poses a risk to the communication network and KKH. The study should assist the concerned agencies in developing and implementing debris flow risk mitigation strategies.

Coupling effects on class C predictions of soft soil settlements

AbstractIn uncoupled consolidation analysis, settlement and pore water pressure are solved independently, whereas in coupled analysis, they are solved simultaneously to ensure continuity (i.e., the volume change in soil due to compression must equal the water volume change caused by dissipation). This study investigates the coupling effects of soil deformation and pore water pressure dissipation in the back analysis of soft soil settlements. It further evaluates the suitability of both coupled and uncoupled constitutive models with different types of monitoring data, providing practical guidance for selecting consolidation models and achieving reliable long-term predictions. The one-dimensional governing equations for soft soil consolidation, incorporating prefabricated vertical drains and creep deformation, are first reviewed. A case study of a trial embankment in Ballina, New South Wales, Australia, is then used to demonstrate the impact of coupling effects and monitoring data on settlement predictions. The results show that considering coupling effects not only improves long-term settlement predictions but also reduces uncertainties in the updated soil parameters, especially when both settlement and pore water pressure data are used.

Analysis and monitoring of the behavior of a rock fill dam ten years after construction: a case study of the Iran-Madani Dam

AbstractIn this study we compared dam monitoring results with those of numerical analysis to propose a plan for the first reservoir impounding of the Iran-Madani Rock fill dam, ten years after the completion of its construction. The stability of the dam body has been assessed using numerical analysis and data obtained from sensors installed in the dam. The correctness and accuracy of the geotechnical parameters of the dam body materials were confirmed by comparing the results of numerical analysis and monitoring through back analysis. The linear correlation coefficients between the experimental data and the numerical results for settlement, pore water pressure, and total stress are 84%, 67%, and 99%, respectively. In addition, the agreement between the design assumptions with both the numerical analysis results and instrumentation data was examined. The arching ratio values obtained from instrumentation and numerical analysis in the core of the dam are 0.47 and 0.35, respectively, indicating the safety of the dam. Finally, a numerical sensitivity analysis was conducted to present a special impounding program for the dam, with a focus on controlling simultaneous changes in pore water pressure and effective stress in the clay core, ten years after the completion of the dam body construction.

Elastic-plastic-creep behavior of high rockfill dams: a case study on Lianghekou CRFD

The instantaneous loading and time-dependent creep deformations occur simultaneously during the lifetime of rockfill dams, but their proportions in monitoring records are still unclear. In previous works, loading and creep were generally simulated independently in turn. This uncoupled method may induce misleading conclusions in interpreting dam behaviors. In this paper, an advanced elastic-plastic-creep model is applied to capture the interaction of loading and creep under complex conditions with varying loading rates based on a benchmark example of 295m-high Lianghekou core wall rockfill dam (CRFD). The instantaneous elastic-plastic parameters of rockfills are calibrated by large-scale triaxial tests (including 300mm and 800mm in diameter) and the time-dependent creep parameters are obtained by back analyses on monitoring records. A series of numerical analyses on the dam are conducted. The loading-creep coupling process of rockfills is successfully captured and the instantaneous and time-dependent creep deformations are explicitly separated. The 800mm-diameter triaxial test weakens the size effect and provides an acceptable representation for the instantaneous behaviors of prototype rockfills. The good agreement between the calculations of the elastic-plastic-creep coupling analysis and measurements indicates creep contributes about 8~15% to total construction deformation. The work provides guides for properly interpreting and evaluating actual dam behaviors.

Back analysis of geomechanical parameters based on a data augmentation algorithm and machine learning technique

Accurate geomechanical parameters are key factors for stability evaluation, disaster forecasting, structural design, and supporting optimization. The intelligent back analysis method based on the monitored information is widely recognized as the most efficient and cost-effective technique for inverting parameters. To address the low accuracy of measured data, and the scarcity of comprehensive datasets, this study proposes an innovative back analysis framework tailored for small sample sizes. We introduce a multi-faceted back analysis approach that combines data augmentation with advanced optimization and machine learning techniques. The auxiliary classifier generative adversarial network (ACGAN)-based data augmentation algorithm is first employed to generate synthetic yet realistic samples that adhere to the underlying probability distribution of the original data, thereby expanding the dataset and mitigating the impact of small sample sizes. Subsequently, we harness the power of optimized particle swarm optimization (OPSO) integrated with support vector machine (SVM) to mine the intricate nonlinear relationships between input and output variables. Then, relying on a case study, the validity of the augmented data and the performance of the developed OPSO-SVM algorithms based on two different sample sizes are studied. Results show that the new datasets generated by ACGAN almost coincide with the actual monitored convergences, exhibiting a correlation coefficient exceeding 0.86. Furthermore, the superiority of the OPSO-SVM algorithm is also demonstrated by comparing the displacement prediction capability of various algorithms through four indices. It is also indicated that the relative error of the predicted displacement values reduces from almost 20% to 5% for the OPSO-SVM model trained with 25 samples and that trained with 625 samples. Finally, the inversed parameters and corresponding convergences predicted by the two OPSO-SVM models trained with different samples are discussed, indicating the feasibility of the combination application of ACGAN and OPSO-SVM in back analysis of geomechanical parameters. This endeavor not only facilitates the progression of underground engineering analysis in scenarios with limited data, but also serves as a pivotal reference for both researchers and practitioners alike.

Probabilistic back analysis of reservoir landslide considering hydro-mechanical coupled observations

Precisely assessing statistical parameters to characterize spatial soil variability presents a significant challenge in probabilistic slope stability analysis, primarily due to inherent soil uncertainties and limited field-specific data. Probabilistic back analysis, recognized as an effective and reliable technique, offers a rational method for utilizing observational data to invert parameters. Nevertheless, previous investigations into parameter inversion for slope stability analysis have seldom considered the coupling of hydro-mechanical characteristics in reservoir landslides. This study proposes a novel integrated Bayesian framework to perform back analysis of reservoir landslides, incorporating the spatial variability of hydro-mechanical parameters. Within this framework, a hypoplastic constitutive model is developed to characterize the step-like deformation of the reservoir slope. The posterior knowledge of the saturated permeability coefficient and shear strength parameters is obtained by collecting field monitoring data of the underground water level and ground displacement. The Maliulin landslide, located in the Three Gorges Reservoir area of China, is used as an illustrative example to validate the proposed framework through back analysis of spatially variable soil parameters and probabilistic stability analysis. The results demonstrate that the proposed Bayesian updating framework significantly reduces the uncertainties associated with statistical values of soil parameters, providing more accurate and reasonable updated soil parameters for probabilistic slope stability analysis.

Embankment base strengthening and construction of working platforms on soft soil

Geosynthetics have been used for decades in infrastructure construction, especially in traffic construction and geotechnics. Despite this, there is still a relatively low uptake of methods for designing solutions using these materials. While the applications of geogrids (or more broadly: geosynthetics) in reinforced soil structures (retaining walls, steep slopes) and the associated design methods are well described in the literature and standards, there is much more modest knowledge available on applications related to the reinforcement of the subsoil directly under road and railway pavement structures, the construction of working platforms on soils with very low bearing capacity, or increasing the fatigue life of the pavement itself. This situation is due, among other things, to the fact that different geosynthetics, including different geogrids, affect the bearing capacity of the subsoil and the durability of the structure in different and often very different ways. Hence – unlike, for example, in the case of retaining walls – it is difficult to develop a one-size-fits-all design method covering this range of applications. It is necessary to rely on the experience of manufacturers of particular varieties of geogrids or geo-grids and to use design methods developed by them based on this experience. This article presents the technology of geocellular mattresses used primarily in the base of embankments on weak and deformable grounds, as well as for the construction of temporary working platforms. The performance of structures in complex and complicated geotechnical conditions is analysed on the example of completed projects. The results of back analysis of structures subjected to real-scale tests are presented, which can be used for preliminary analysis of solutions in numerical modelling programmes

Failure analysis and optimization design of CFRP automotive seat back under multiple conditions

This study explores the adoption of carbon fiber reinforced plastic (CFRP) in automotive seat backrests, aiming to improve compliance with regulatory standards and enhance performance. By replacing conventional materials with CFRP, and employing mechanical performance tests alongside the principle of equal stiffness, the study tackles material failure issues. A novel multi-criteria decision-making method, coupled with an optimal Latin hypercube experimental design, is used to optimize the layup sequence. The optimized design not only meets regulatory requirements but also significantly enhances driver and passenger safety and comfort during collisions.

Enhancing ground loss rate prediction in soft-soil shield tunneling: a synergistic approach of peck back analysis and eXtreme Gradient Boosting and bayesian optimization

This study investigates the prediction of ground loss rate during soft-soil shield tunneling using Peck’s back analysis method and XGBoost model. Bayesian optimization is employed to determine optimal hyperparameters, ensuring comprehensive and efficient model tuning. The XGBoost model is compared with Random Forest (RF) and Support Vector Machine (SVM) models to benchmark its performance. The results demonstrate the superior accuracy and robustness of the XGBoost model. Also, the results show that the soil properties and the grouting factors of the excavation face affect the duration of the instantaneous settlement of the ground surface. There is a specific correlation between the depth-to-diameter ratio, the coefficient of variation in the advancing speed of the shield machine, the maximum surface subsidence, and the ground loss rate. The prediction model of the ground loss rate based on the combined approach of Peck back analysis and eXtreme Gradient Boosting and Bayesian optimization has high reliability in soft-soil layers, and this method can provide a specific reference for predicting construction risk in related projects.

Numerical Simulation of Effects of Previous PVD Treatment on Second Vacuum-PVD Improvement

The influence of the initial improvement through prefabricated vertical drains (PVD) and surcharge embankment on the second improvement by vacuum PVD has not been comprehensively explored in the existing literature. The expansion of the Second Bangkok International Airport (Suvarnabhumi Airport) underwent initial treatment with PVD and surcharge, followed by further soft ground improvement with vacuum PVD, which serves as a valuable case study for understanding the impact of the initial PVD improvement. This study highlights the effects of the initial PVD improvement through a comparison of two case studies: the first improvement by vacuum PVD on Taxiway Extension D at Zone 27 and the second vacuum PVD improvement on the Third Extension North at Zone 7. Extensive back analysis results reveal significantly higher flow parameters and reduced settlements for the second improvement at Zone 7 compared to the first improvement at Zone 27. The water contents, void ratios, and compression indices decreased while the undrained shear strengths and maximum past pressures increased. These results demonstrated the effectiveness of the vacuum PVD improvement with a modified field-distributed air-water separation system. Numerical studies further corroborated these findings and confirmed the observed crack patterns at the surface resulting from inward lateral movement.

Improved Bayesian model updating of geomaterial parameters for slope reliability assessment considering spatial variability

In engineering practice, Bayesian model updating using field data is often conducted to reduce the substantial inherent epistemic uncertainties in geomaterial properties resulting from complex geological processes. The Bayesian Updating with Subset simulation (BUS) method is commonly employed for this purpose. However, the wealth of field data available for engineers to interpret can lead to challenges associated with the “curse of dimensionality”. Specifically, the value of the likelihood function in the BUS method can become extremely small as the volume of field data increases, potentially falling below the accuracy threshold of computer floating-point operations. This undermines both the computational efficiency and accuracy of Bayesian model updating. To effectively address this technical challenge, this paper proposes an improved BUS method developed based on parallel system reliability analysis. Leveraging the Cholesky decomposition-based midpoint method, the total failure domain in the original BUS method, which involves a low acceptance rate, is subdivided into several sub-failure domains with a high acceptance rate. Facilitated with an improved Metropolis-Hastings algorithm, the improved BUS method enables the consideration of a large volume of field data and spatial variability of geomaterial properties in the probabilistic back analysis. The results of an illustrative soil slope, involving spatially variable undrained shear strength, demonstrate that the improved BUS method is effective in simultaneously incorporating a substantial volume of field measurements and observations in the model updating process. Through a comparison with the original BUS method, the improved BUS method is shown to be useful for Bayesian model updating of high-dimensional spatially variable geomaterial properties and slope reliability assessment.

Cryogenic tensile behavior of CoNiCrMo medium entropy alloy

The microstructure and mechanical properties of 35Co-34Cr-24Ni-7Mo medium entropy alloy, known as MP35N, were investigated at different cryogenic temperatures (77 K and 195 K) and compared with those at room temperature (295 K). The microstructural features at the mentioned three distinct temperatures were characterized by X-Ray diffraction, electron back skater diffraction analysis and transmission electron microscopy. It was found that, compared to room temperature, cryogenic temperatures significantly increased the yield strength and tensile strength from 330 MPa and 1300 MPa at 295 K to 711 MPa and 1890 MPa at 77 K, respectively, without a noticeable change in elongation. Microstructural investigation revealed that lower deformation temperatures resulted in higher dislocation density, higher stacking fault probability, more mechanical twins, and a more homogeneous dislocation structure. Up to a strain of 0.4, no sign of fcc to hcp phase transformation was observed in EBSD, TEM images, or SAED patterns, even at 77 K. The higher work hardening due to mechanical twinning, increased dislocation density, and lower stacking fault energy, in addition to a more homogeneous dislocation structure, postponed the formation of geometric instabilities and maintained a ductile fracture mechanism at cryogenic temperatures.

Slope stability modeling using limit equilibrium and finite element methods: A case study of the Adama City, Northern Main Ethiopian Rift

Slope failure is a prominent and recurring geohazard in numerous parts of Ethiopia, including Adama City, which is located in the Northern Main Ethiopian Rift (NMER). The city is surrounded by two ridges oriented in the NNE-SSW direction, which are susceptible to slope instability. Thus, this study is aimed at modeling slope stability along these two ridges using Finite Element Method (FEM) and Limit Equilibrium Method (LEM). The modeling was carried out on slopes of multifaceted geometry composed of eluvium soil, pumice, and moderately to highly-weathered ignimbrites. Critical slope sections were identified using satellite imagery and field manifestations such as slope toe condition and slope face tilting. Their geometries were then inferred from detailed geological cross-sections based on field data. Input parameters for the modeling, such as cohesion, friction angle, and elastic modulus, were calculated via Back Analysis using the Hoek-Brown criterion while unit weight and Poisson ratio were determined from empirical equations. For soil formations, the parameters were determined via standard laboratory experiments. The modeling was then carried out under different conditions, including dry, saturated, static, and dynamic conditions. Results from both LEM and FEM models revealed that three of the four analyzed slope segments were unstable under dynamic and saturated conditions, highlighting the influence and importance of precipitation and seismicity as triggering variables. Results from both methods tend to agree when the critical slip surface passes through a single geological material in both models. However, notable differences arise when the slip surface involves multiple geological materials. Under such conditions, LEM tends to yield higher FOS values compared to FEM. The results also showed that all unstable slopes were associated with the NNE-SSW striking fault of the study area, as inferred from failure surfaces generated from both models and field data. The study concluded that unstable slopes pose a serious risk to nearby residents and infrastructure, and as a remedy, it designed and recommended coupled benching and slope flattening.

Probability analysis of vertical drainage improvement for soft soil settlement prediction using a Bayesian back analysis framework and the simplified Hypothesis B method

Probability analysis of vertical drainage improvement for soft soil settlement prediction using a Bayesian back analysis framework and the simplified Hypothesis B method

Parameter determination of Johnson–Holmquist–Cook constitutive model and calibration for Indiana Limestone

In this study, a comprehensive parameter determination procedure for the Johnson–Holmquist–Cook (JHC) constitutive model is introduced, including calibration and validation processes for Indiana Limestone rocks. The procedure is conducted utilizing the existing physical and mechanical properties of Indiana Limestone. To obtain an accurate set of parameters for the JHC model for Indiana Limestone, an extensive dataset comprising mechanical and physical properties of Indiana Limestone rocks was initially compiled. The static mechanical tests incorporated uniaxial compression, triaxial compression, direct tensile, and uniaxial strain data, while the dynamic mechanical test data was primarily derived from the Split Hopkinson Pressure Bar experiments. Subsequently, the JHC constitutive model parameters were determined using existing literature data, employing statistical analysis, theoretical derivation, and numerical back analysis techniques. One of the damage parameters was determined through numerical post-peak behavior calibration of triaxial compression strength test results on experimental data. Finally, the accuracy of the determined parameters was validated by comparing the numerical and experimental results of both static and dynamic tests. This study effectively addresses the challenges associated with the numerical method using the JHC material model, such as the complex parameter determination process and the costly required tests, thereby preserving the efficiency and applicability of the numerical method.

Simulation of geological uncertainty based on improved three-dimensional coupled Markov chain model

The natural stratigraphic distribution is uncertain due to the geological tectonic movements and sedimentation. It is a challenge for traditional deterministic models to capture geological uncertainty. The objective of this study is to develop an improved three-dimensional coupled Markov chain (ICMC3D) model based on limited boreholes. The proposed method enhances the traditional three-dimensional coupled Markov chain model by modifying the transition probability equation for predicting unknown elements. The dip data is added to estimate the transition probability matrix with the borehole information. On this basis, a back analysis method is proposed for estimating the horizontal transition probability matrics. The method makes the predicted results more suitable for the possible stratigraphic distribution. The accuracy of the proposed method is verified through some applications. In this paper, an improved three-dimensional coupled Markov chain model and transition probability matrix estimation method are illustrated by the borehole data collected from a tunnel project in Qingdao. The results show that the presented method can reflect the asymmetry, continuity and anisotropy of the three-dimensional stratum. The exceedance probability is proposed to predict the fault zone development range in order to reduce the geological uncertainty.

An adaptive multi-output Gaussian process surrogate model for large-scale parameter estimation problems

PurposeParameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.Design/methodology/approachThe MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.FindingsThe numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.Originality/valueA novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

Seroja Tropical Cyclon Destruction Case Study: Permanent Slope Protection Evaluation at Downstream of Rotiklot Dam Nomenclature Building, East Nusa Tenggara, Indonesia

Abstract Rotiklot Dam was one of some infrastructures which was affected by the Seroja Tropical Cyclone in East Nusa Tenggara Indonesia in April 2021. There are several landslides at the downstream of the dam that can threaten the safety of the dam, especially at the downstream of the nomenclature building. The right design of permanent slope protection must be determined in a limited time. Geotechnical investigations such as boring, resistivity survey, standard penetration test, and water pressure test were conducted in a short time. Back analysis method was carried out to find extreme soil parameters conditions. The slope protection must meet the national standard minimum safety factor or greater than 1.5. Safety factors of slope protections are evaluated by a finite element method to get the safety factor and maximum total displacements. Every stage of slope protection can give the optimal safety factor values which are required or greater than 1.5, however backfill material will give more load and decrease the safety factor value.