Search

Rapid and partial acquisition are features of rock drilling for obtaining rock properties. Most previous research has primarily concentrated on how to quickly obtain rock mechanics parameters, with limited emphasis on extracting rock parameter fields, particularly in three dimensions. This study attempted to develop a numerical integrated method to extract 3D parameter fields of rocks based on a newly developed digital-controlled drilling platform. The importance of incorporating a damage model for accurate simulations of rock drilling through finite element analysis (FEA) was investigated. By calibrating damage parameters through uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) tests, these parameters can be considered constants in rock drilling simulations across various rock types. The accuracy of rock parameters estimated by the proposed method and the derived analytical model were further demonstrated through comparison with the corresponding standard tests. Furthermore, the 3D parameter field of rocks was obtained by integrating a deep learning method and micro-CT technology. The numerical prediction illustrated the advantages of acquiring a rock parameter field in achieving more accurate simulations of the rock failure process. Besides, our solution can also provide support for the parameter selection of numerical models considering spatial variability for natural rocks. Highlights: A digital-controlled equipment for rock drilling was developed. Equations were derived for extracting rock parameters through the drilling data. A deep learning method was developed to reproduce the three-dimensional parameter field of rocks. The significance of considering the realistic rock parameter field was numerically demonstrated. [ABSTRACT FROM AUTHOR]

The creep behavior of rocks has been broadly researched because of its extensive application in geomechanics. Since the time-dependent stability of underground constructions is a critical aspect of geotechnical engineering, a comprehensive understanding of the creep behavior of rocks plays a pivotal role in ensuring the safety of such structures. Various factors, including stress level, temperature, rock damage, water content, rock anisotropy, etc., can influence rocks' creep characteristics. One of the main topics in the creep analysis of rocks is the constitutive models, which can be categorized into empirical, component, and mechanism-based models. In this research, the previously proposed creep models were reviewed, and their main characteristics were discussed. The effectiveness of the models in simulating the accelerated phase of rock creep was evaluated by comparing their performance with the creep test results of different types of rocks. The application of rock's creep analysis in different engineering projects and adopting appropriate creep properties for rock mass were also examined. The primary limitation associated with empirical and classical component models lies in their challenges when it comes to modeling the tertiary phase of rock creep. The mechanism-based models have demonstrated success in effectively simulating the complete creep phases; nevertheless, additional validation is crucial to establish their broader applicability. However, further investigation is still required to develop creep models specific to rock mass. In this paper, we attempted to review and discuss the most recent studies in creep analysis of rocks that can be used by researchers conducting creep analysis in geomechanics. Highlights: Creep constitutive models for rocks were reviewed, and their main characteristics were discussed. The applications of creep analysis in geomechanics were explained, and some engineering projects were mentioned. The back analysis techniques using long-time measured monitoring data were successfully used for finding rock mass creep parameters. [ABSTRACT FROM AUTHOR]

The cost of a landslide early warning system (LEWS) is a significant hurdle in its installation and adoption in developing nations like India, which has one of the highest rates of landslide occurrences worldwide. Previous research has focused on using low-cost monitoring instruments, cost-effective data transmission-management technologies, open-source technology, etc., to reduce the cost of LEWS. A possible strategy for reducing the price of a LEWS is to restrict the area that needs to be monitored. The cost of LEWS directly relates to the size of the slide. For instance, a massive landslide requires additional sensors to monitor the entire slide. However, for large landslides where progressive failure is confined to a particular zone, we must concentrate monitoring efforts on these critical areas for the optimal allocation of resources. For such slides, the number of sensors required can be reduced by focusing the deployment of sensors on the most vulnerable areas, leading to a lower cost. Hence, the current study proposes a generalized method for identifying potential failure zone/areas in large or deep-seated landslides that need to be monitored. This method not only provides economization of the number of sensors required but also ensures that data collection is focused and relevant, potentially enhancing the quality of monitoring and the accuracy of predictive models. The proposed method integrates different geotechnical approaches such as field investigation, laboratory testing, back analysis, and multi-temporal stability analysis. The method was tested on a deep-seated Kotropi landslide Himachal Pradesh initiated in 2017 and continuously experiencing progressive failures. A multi-temporal stability analysis was conducted in two phases. The first phase utilizes data collected during the 2018 field visit and estimates the probability of failure in different areas of the landslide. A field visit successfully validated the failure zone identified in the first phase. Furthermore, the second phase stability analysis, based on the data collected during the 2022 field visit, was performed to determine the future probability of failure in different slide areas. The in-depth analysis indicates that the Kotropi landslide is experiencing progressive failure, which limited to a particular zone in N and NW direction in contrast to the initial failure in NE-SW direction. Hence, using the proposed method, the area of a large landslide that needs to be monitored can be reduced by identifying the most vulnerable area, lowering the overall cost of a LEWS. [ABSTRACT FROM AUTHOR]

Efficient and accurate acquisition of slope shear strength parameters is the key to slope stability analysis and landslide prevention engineering design. This paper establishes a back analysis method based on uniform design, artificial neural network, and genetic algorithm. It can obtain the shear strength parameters of slopes based on information such as the radius and center coordinates of the slip surface obtained from on‐site investigations. This method has been applied to engineering practice. The research results indicate that the stability of the waste dump slope is most sensitive to the response of the internal friction angle of the loose body, followed by cohesion, and least sensitive to the response of the soil volume weight. This method can effectively reduce the number of network training samples and efficiently and quickly determine the initial weights of the BP (abbreviation for back‐propagation) neural network. This method can efficiently and quickly conduct back analysis to obtain the shear strength parameters of slopes. Using the obtained shear strength parameters for slope stability calculation, the most dangerous slip surface abscissa error, ordinate error, and slip surface radius error are only 3.59%, 0.95%, and 1.83%. It is recommended to promote the back analysis method of shear strength parameters in engineering practice in the future. [ABSTRACT FROM AUTHOR]

Continuous structural health monitoring of long-span bridges, such as suspension bridges, is essential for ensuring stability, and various efforts have been undertaken towards this goal. The hanger cables of suspension bridges play a crucial role in transmitting the main loads within the cable support structure. Therefore, monitoring the tension of hanger cables is vital for maintaining structural stability. Traditionally, the vibration method has been utilized for operational suspension bridges to indirectly estimate the tension of suspension bridge hanger cables. This method involves measuring the natural frequencies for vibration modes from the cables and their geometric conditions. In this study, digital camcorders and tripods were employed to measure the hanger cable response conveniently and efficiently. The response measured by digital camcorders is displacement based, posing challenges in measuring the natural frequencies for high-order modes required by the vibration method. Typically, systems for measuring structural response incorporate white noise components across all frequency domains, complicating the identification of cable frequency component characteristics when the response frequency measured by a digital camcorder is lower than the white noise frequency. Furthermore, measurements taken with a digital camcorder may suffer from low resolution due to long distances and optical limitations, hindering the measurement of high-frequency response components. To address these challenges, this study introduced a back analysis method to estimate tension using the natural frequencies of low-order modes. The method defines the difference between measured natural frequencies and those predicted by finite-element analysis as the error function. Optimization was then conducted using the univariate search method to minimize this error function. The findings suggest that the tension estimated by applying only the first natural frequency of hanger cables through the back analysis method closely aligns with estimates obtained using the vibration method. This research highlights the potential of using standard digital camcorders as an accurate and cost-effective means for estimating hanger cable tension. [ABSTRACT FROM AUTHOR]

For today’s demands in underground infrastructure, geotechnical considerations are gaining prominence, emphasizing the resolution of complex problems through numerical modeling and soil-structure interaction analysis. There is a clear trend shifting away from two-dimensional (2-D) plane strain or axisymmetric idealizations to the comprehensive analysis of full three-dimensional (3-D) models. However, despite the inclination towards 3-D modeling of tunnels, establishing a realistic 3-D model for this purpose remains a timeconsuming process. This typically involves multiple iterative and incremental phases, essential for gaining deeper insights into the three-dimensional nature of the problem. Beyond selecting an appropriate constitutive soil model, realistically finite element modeling and accurate simulation of the tunneling process are of paramount importance. This study focuses on the development of a practical full 3-D model and simulation of the Earth Pressure Balance (EPB) shield tunneling process using the finite element code PLAXIS 3D. The numerical model incorporates tunnel boring machine driving data recorded during tunnel construction and is validated through back-analysis of field measurements of tunneling-induced ground surface settlements. The findings demonstrate that numerical simulation can realistically reproduce the main characteristics of EPB shield tunneling. The study recommends adopting non-linear face pressures with depth based on the analysis of tunneling data. Regarding grouting pressure, an effectively linear distributed pressure may be considered, accounting for the grout weight and the increase of soil stresses with depth. This validated numerical framework provides valuable guidance for researchers and practitioners to construct more efficient, reliable, and accurate 3-D models. [ABSTRACT FROM AUTHOR]

The construction process of the Tilong Dam induced significant settlement in the surrounding areas. This study aimed to enhance our understanding of the factors that influenced settlement behavior and improve the reliability of predictive models by using instrumentation data regarding the dam's vertical displacement. Numerical modeling was conducted to consider the actual embankment conditions and analyze the changes in the immediate settlement during and after construction. After the Tilong Dam construction, the measurements and numerical analysis showed settlements ranging from 0 to 832 mm. It was noted that settlements were expected during and after the construction, particularly at the dam's mid-sections. The back analysis method was employed to establish the values for material properties, specifically Young's modulus and Poisson's ratio, which affected the displacement. A multivariate regression comparison between the observed and predicted data yielded a determination coefficient (R²) of 0.914. This indicated a strong correlation between the observed and predicted values.

Slope failures in mining engineering pose significant risks to slope stability control, necessitating a thorough investigation into their root causes. This paper focuses on a back analysis of a slope failure in the Zerga section of the Ouenza – Algeria open-pit iron mine. The primary objectives are to identify the causes of slope failure, propose preventive measures, and suggest techniques to enhance stability, thereby providing crucial insights for monitoring slope stability during mining operations. The study commenced with a reconstruction of the slopes in the affected zones, followed by a numerical analysis utilizing the Shear strength reduction method within the Finite element method (SSR-FE). This approach enables the examination of slope stability under both static and dynamic loads. The dynamic load assessment incorporated an evaluation of the vibrations induced by the blasting process during excavation, introducing seismic loading into the finite element analysis. The findings reveal that the primary triggering factor for the landslide was the vibration generated by the blasting process. Furthermore, the slope stability was found to be critically compromised under static loads, highlighting a failure to adhere to exploitation operation norms. The challenging geology, particularly the presence of marl layers where maximum shear strain occurs, contributed to the formation of the landslide surface. The study not only identifies the causes of slope failure but also provides valuable lessons for effective slope stability management in mining operations.

Forecasting landslide failure time is important for understanding the kinematics of landslides. In addition to the famous inverse velocity method (INV), other methods such as the minimum inverse velocity method (MINV), slope gradient (SLO), and velocity over acceleration (VOA) have been proposed. Despite the importance of adopting a forecasting technique, very limited insights regarding the mentioned methods have been published. This study aims to address this gap by conducting a comparative examination of INV, MINV, SLO, and VOA on a comprehensive database of 75 historical landslides. This access to a large volume of historical data enabled us to assess the advantages and limitations of each method. Results show that MINV is the only method with better performance than INV, by 23% on average and, in 32% of cases, by at least 100%. On the other hand, the accuracy of SLO forecasts is on average 87% worse than INV forecasts. VOA results have a 6.84 to 11.68 times higher error compared to INV. VOA is highly sensitive, even more so than INV, to scatter in measurements, but the positive effect of filtration is offset by data artifacts caused by the data filtration technique. SLO, owing to its mathematical configuration, is rather insensitive to measurement scatter. Finally, the failure velocities, as calibrated by MINV, are presented for each failure mode; this can enhance the detection of the onset of acceleration and be used toward more adaptive warning thresholds in monitoring systems depending on the site setting. [ABSTRACT FROM AUTHOR]

In open-pit mining operations where activities are conducted below water table or close to surface water bodies, the mine is potentially faced with dewatering and slope stability challenges. Appropriate evaluation of slope stability to obtain adequate slope safety factor has continuously been the centre of geotechnical research locally and internationally. This paper evaluates the influence of buoyant action propagated by groundwater and surface (GW-SW) interaction on pit slope designed in multi-layered fractured rock with complex hydrology using Hydrogeological-Stratigraphic-Structural Assessment (HSSA) at the Nsuta Manganese Mine in Ghana. LEM analyses of the Pit C-western slope estimated an overall factor of safety (FOS) of 1.1, probabilities of failure (POF) of 7.5% and the reliability index (RI) of 1.4 in the slope stress field. However, when the groundwater was introduced, the slope FOS was reduced to 0.13, POF of 100.0% and (RI) of 0.00 indicating failure. FEM numerical model points to the location of maximum shear strain and total displacement in the argillite zone within the rock mass. The FEM seepage analysis model estimated groundwater pore pressure data along the critical slide surfaces from the crest of the slope to the bottom of the rock slope ranging between 250 kPa and 4250 kPa with an average pore pressure of 2250 kPa within the pit slope. This proposed HSSA approach used in the study provides innovative insights into the percentage failure contribution of the individual lithologies to the overall combined lithological susceptibility of the slope under stress and groundwater failure mechanisms, pointing out Ru ratio in pit slope stability estimation as inadequate. [ABSTRACT FROM AUTHOR]

To investigate the potential use of the complex function theory in displacement back analysis, a novel method for parameter identification is proposed by combining the complex function theory and the back-propagation neural network optimized by the particle swarm optimization algorithm. The finite element method is replaced with the complex function method to establish a nonlinear relationship between parameters and deformations of the surrounding rock around a complex underground cavern. As demonstrated by a virtual example of an arched tunnel, the deformation parameters were identified by the proposed method and demonstrated to be approximately equal to the predefined parameters. Subsequently, the proposed method is further applied to the inverse analysis of mechanical parameters for the surrounding rock in the underground powerhouse of the Baihetan Hydropower Station. The computed displacements based on the back-analyzed parameters show excellent agreement with the monitoring displacements. The average error between them is about 4.17%, so the proposed method provides the potential to viably enhance the back-analysis technique. [ABSTRACT FROM AUTHOR]

The effectiveness of tunnel monitoring is a challenging task due to the limitations of monitoring gauges and lack of monitoring sections. To address this, a novel theoretical analysis-based monitoring method for tunnel structures was proposed in this study. A theoretical approach was employed to establish the correlation between external loads and structural stress–strain response in tunnel lining during grouting and stability periods. A method has been developed to derive the distribution of external loads and internal forces throughout the entire tunnel using strain monitoring at specific locations on the structure. This method has been further validated through a case study of the Liucun Tunnel, providing insights into the accuracy of the monitoring approach. It is found that during the grouting period, the segment ring is surrounded by grout, resulting in peak external loads and internal forces. As the tunnel lining enters the load stability period, both the external loads and internal forces gradually decrease and stabilize. Comparing the results of the monitored method for deriving tunnel external loads, structural bending moments and axial forces with the on-situ measurements, the new monitoring method yields errors in the response of tunnel external loads and internal forces. The average error in external loads is less than 12%, the average error in bending moments is less than 20%, and the average error in axial forces is less than 8%. The proposed monitoring method effectively addresses the issue of long-term failure of monitoring elements due to its replaceability. Additionally, utilizing theoretical methods for derivation allows obtaining more tunnel structural information based on limited monitoring data from the elements. This provides a new approach for long-term structural health monitoring. To address the existing errors in the monitoring method described in this study, the accuracy can be further improved by optimizing the model, incorporating more advanced monitoring techniques, and implementing standardized and improved construction practices. [ABSTRACT FROM AUTHOR]

Landslides are frequent and devastating geological hazards that cause significant economic and human losses annually. The selection of appropriate shear strength parameters is crucial for evaluating and preventing landslide failure. Considering the nonlinear characteristics of rock-mass material, a rock slope stability analysis model was built using the nonlinear Hoek‒Brown failure criterion. A Geological Strength Index–material constant (GSI–mi) curve, based on a deterministic method, was proposed to back analyzed the Geological Strength Index (GSI), which is consistent with a trial-and-error method, but simpler to implement. The back analysis can be extended to other parameters in addition to the GSI. Furthermore, considering the randomness and uncertainty, a reliability-based back analysis method for nonlinear strength parameters of the sliding surface was proposed. The optimal parameter combination that corresponded to the minimum reliability index was determined, which could serve as reasonable values of the nonlinear strength parameters for landslides. A comparison of different failure criteria also suggested that the stress level at the sliding surface should be accounted for in landslides using the significant nonlinear characteristics of strength parameters. [ABSTRACT FROM AUTHOR]

Abstract Efficient and accurate acquisition of slope shear strength parameters is the key to slope stability analysis and landslide prevention engineering design. This paper establishes a back analysis method based on uniform design, artificial neural network, and genetic algorithm. It can obtain the shear strength parameters of slopes based on information such as the radius and center coordinates of the slip surface obtained from on‐site investigations. This method has been applied to engineering practice. The research results indicate that the stability of the waste dump slope is most sensitive to the response of the internal friction angle of the loose body, followed by cohesion, and least sensitive to the response of the soil volume weight. This method can effectively reduce the number of network training samples and efficiently and quickly determine the initial weights of the BP (abbreviation for back‐propagation) neural network. This method can efficiently and quickly conduct back analysis to obtain the shear strength parameters of slopes. Using the obtained shear strength parameters for slope stability calculation, the most dangerous slip surface abscissa error, ordinate error, and slip surface radius error are only 3.59%, 0.95%, and 1.83%. It is recommended to promote the back analysis method of shear strength parameters in engineering practice in the future.

Accurate prediction of thermal conductivity (λ) in frozen soils is essential for understanding their thermal behavior and improving thermal response analyses. Currently, there are numerous models for calculating λ, but unified laboratory data to compare and evaluate these models are lacking. This study focuses on evaluating the performance of 17 models for applied to frozen soils using a unified laboratorial dataset. The heat-pulse-probe (HPP) method was employed to measure the thermal conductivity of silty clay, sand, and sand loam. Nuclear magnetic resonance experiments were conducted to measure the unfrozen water content (θw) and ice content(θi). This provides unified laboratorial dataset of λ-θi-θw. The predictive performance of 17 models demonstrated that the model of Zhang et al. (2018), Sass et al. (1971) and Tian et al. (2016) are the three optimal models: 1). The model of Zhang et al. (2018) is the provided the best predictive performance, with mean absolute error (MAE) values of 0.17 Wm-1K-1, 0.07 Wm-1K-1, and 0.06 Wm-1K-1 for silty clay, sand and sand loam, respectively. This model is advantageous due to its simplicity and the absence of undetermined parameters. 2). The model of Sass et al. (1971) is suitable for soils with low sensitivity to frozen volume deformation, such as sand and sand loam, achieving an MAE of 0.06 Wm-1K-1. 3). The model of Tian et al. (2016) exhibited strong performance for silty clay with an MAE of 0.11 Wm-1K-1, contingent on the precise determination of soil-specific parameters. This study provides a unified laboratory data for the establishment of thermal conductivity models. The evaluation of existing models can serve as a reference for future related research.

Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety. This paper proposes a modified back analysis method with multi-objective optimization procedure, which enables a real-time prediction of horizontal displacement of retaining pile during construction. As opposed to the traditional stage-by-stage back analysis, time series monitoring data till the current excavation stage are utilized to form a multi-objective function. Then, the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification. The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages. To achieve efficient parameter optimization and real-time prediction of system behavior, the back propagation neural network (BPNN) is established to substitute the finite element model, which is further implemented together with MOPSO for automatic operation. The proposed approach is applied in the Taihu tunnel excavation project, where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data. The method is reliable with a prediction accuracy of more than 90%. Moreover, different optimization algorithms, including non-dominated sorting genetic algorithm (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO, are compared, and their influences on the prediction accuracy at different excavation stages are studied. The results show that MOPSO has the best performance for high dimensional optimization task.

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson's ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns. [ABSTRACT FROM AUTHOR]

The investigation of seepage situation of a reservoir under complex geological conditions is greatly significant and is necessary to be determined by geologic survey or numerical analysis. Based on previous geological survey data, a 3-dimensional seepage back analysis was conducted. In this study, two hydraulic conditions of unsteady and steady seepage were considered, and the corresponding back analysis came down to a multi-objective decision-making problem. The GRNN model was trained by PSO algorithm for obtaining the relationship between permeability coefficients and monitoring data, and by combining the NSGA-II algorithm, the best unbiased solution of permeability coefficients for the multi-objective function established by monitoring data of seepage discharge and pressure head was searched via iteration calculation. On this basis, the seepage safety of the reservoir was evaluated. Through taking the seepage discharge of the whole reservoir into account, the problem in the traditional seepage field back analysis that the foundation strata seepage parameters are insensitive to the pressure head inside the dam was solved. And the inversion results can provide a supplement for the geological survey under complex geological conditions. The method adopted in this paper may provide significant references for the anti-seepage design and reinforcement of hydraulic structures with complex geological conditions. [ABSTRACT FROM AUTHOR]

Landslides govern the evolution of landforms and pose a serious threat across the globe, especially in mountainous areas. In the northwestern area of Vietnam, a slow-moving landslide occurred near an important economic road corridor in Caumay Ward, Sapa Town, Laocai Province. In December 2019, some serious cracks were observed at a construction site near this landslide. Since this phenomenon could cause not only loss of life but also damage to the properties located downhill, the construction was abandoned until the slope was rehabilitated. Geological investigations, laboratory tests, and surface displacement monitoring were conducted to understand the failure mechanism. The analysis results showed that the anthropogenic activities associated with the rising groundwater level due to frequent rainfall events, owing to climate change, had contributed to the sliding of the sloping soil mass. The rehabilitation works at the failed area were conducted chronologically in two stages: (1) backfilling at the downhill area; demolishing two villas located within the sliding area to reduce surcharge; constructing an anchor system in the uphill area, and (2) constructing the anchored wall at the downhill area. During the rehabilitation works, the Caumay landslide was observed to initially undergo gradual movement and then stabilize at the end of the first stage of the rehabilitation works. The rehabilitation techniques adopted at the failed site were validated using both numerical analysis and field measurements. The anchor reinforcement methodology adopted in this study is expected to help agencies and the public in stabilizing landslide-prone areas for residential and other infrastructure construction. [ABSTRACT FROM AUTHOR]

Measurement and model uncertainties in soil parameters account for the difference between slope behaviour in the field and expected behaviour. The probabilistic back analysis is an effective approach to quantify these uncertainties in soil parameters. A new methodology for probabilistic back analysis is proposed to evaluate the uncertainties in soil parameters for observed data for slope. The proposed methodology implements multi-output least square support vector regression (MLS-SVR) to replicate the numerical model for slope under precipitation. This methodology also utilises a multi-objective genetic algorithm and Bayesian analysis to estimate updated statistics of soil parameters for observed data for slope. The rainfall-induced slope failure at Malin, Pune, India, in 2014 is used as a case study to validate the proposed methodology. The mean values of soil parameters are updated using multi-objective genetic algorithm for the expected values of safety factor. The uncertainties in soil parameters are estimated using Bayesian analysis. The updated statistics of input parameters suggest that matric suction governs the slope behaviour under rainfall precipitation. The results of the study suggest that continuous updating of the observations reduces the uncertainties involved in soil parameters. It is noted that the values of safety factor calculated using updated parameters are consistent with the slope failure observed in the field. Hence, results of the study can be used for the reliability-based design of slopes and the provision of remedial measures. [ABSTRACT FROM AUTHOR]

A reasonable tunnel construction scheme is the key to preventing tunnel accidents and reducing cost waste. Aiming at a series of problems such as geological uncertainty, blindness of the construction scheme, and abstraction of construction data faced by tunnel construction, this paper uses building information modelling (BIM) technology, the Internet of things (IoT) technology, and particle swarm optimization (PSO) algorithm to establish an integrated intelligent system framework for tunnel information feedback construction. The idea of integration of this platform is introduced first. Furthermore, BIM tunnel IFC extension information integration technology, improved tunnel multi-information monitoring IoT technology, back-analysis of surrounding rock parameters, and optimization of the construction scheme based on PSO were discussed in detail. The tunnel construction feedback analysis process is proposed on this platform. The integrated system framework is applied to the construction of the Zhenfengling tunnel in Jilin Province, China, demonstrating that the proposed intelligent integration framework is effective. The IoT monitoring and the BIM information integration functions are feasible. Feedback analysis of anchoring parameters is fast and accurate. This platform integrates the advantages of multiple current technologies, reflects the trend of intelligence, visualization, and automation in tunnel information construction, and is of great significance for the advancement of digital and intelligent tunnel construction technology. The results of the anchoring parameters provide references for the tunnel construction studied. [ABSTRACT FROM AUTHOR]

Cascading landslides are sequences of multiple landslides that commonly involves significant volumes of material and exhibit variable velocity up to several m/s. The impact of these processes is generally significant so that they can claim victims and be responsible of significant losses. Considering the complexity of the process, their mitigation involves the understanding of causeeffect relations between the initial triggering event and subsequent cascading processes as well as the development of methodological framework for their analysis. On this basis, this work aims to analyse the characteristics of the cascading landslide event of Morino-Rendinara, in the L’Aquila province, applying a procedure that, comprising multiple methods, is capable of providing data depicting mechanism and kinematics of the system, anatomy of landslides and prospective susceptibility scenario. [ABSTRACT FROM AUTHOR]

In the last decades the Valtellina valley (northern Italy) has suffered from several catastrophic rainfall-induced shallow landslide events inducing debris flows. The growing of urban settlements has driven population to colonize areas at risk, where prediction and prevention actions are nowadays a challenge for geoscientists. Debris flows are widespread in mountain areas because occurring along steep slopes covered by loose regolith or soil coverings. Under such conditions, heavy rainfall events might cause slope instabilities due to the increase in pore water pressure depending on hydraulic and geotechnical properties as well as thicknesses of soil covers. Despite the initial small volumes, debris flows hazard is significant due to the sediment entrainment and volume increase of the involved material, high velocity and runout distance. In such a framework, predicting timing and position of slope instabilities as well as paths, volumes, and velocity of potential debris flows is of great significance to assess areas at risk and to settle appropriate countermeasures. In this work, back analyses of debris flows occurred in representative sites of the Valtellina valley were carried out with the aim of understanding their features and providing a methodological basis for slope to valley scale susceptibility mapping. Numerical modeling of slope stability and runout was completed allowing the identification of the detachment, transport, and deposition zones of previously occurred landslides, including other potentially unstable ones. Results from this study emphasize issues in performing distributed numerical modeling depending on the availability of spatially distributed soil properties which hamper the quality of physicsbased models. In the framework of hazard mapping and risk strategy assessments, the approach presented can be used to evaluate the possible runout phase of new potential debris flows recognized by geomorphological evidence and numerical modeling. Furthermore, analyses aimed to the probabilistic assessment of landslide spatial distribution, related to a specific value of rainfall threshold, can be considered as potentially applicable to multi-scale landslide hazard mapping and extendable to other similar mountainous frameworks. [ABSTRACT FROM AUTHOR]

A landslide was experienced in Karyamekar Village, Cilawu District, Garut Regency, West Java, on 12 February 2021 at approximately 300 m length with a depth of 20 m, leading to a steep slope. Therefore, this study aimed to use 3D back analysis to determine soil shear strength to be subsequently applied in analyzing the possibility of further landslide with due consideration for tension crack and rainfall effect. It was also used to understand the influence of these factors on slope stability. Filled tension crack and rainfall effects were modeled using Finite Element Method (FEM) while Limit Equilibrium Method (LEM) was applied for back analysis. The results of back analysis showed that peak shear strength value of f was 31.18° at a cohesion of 8.01 kPa while the residual shear strength value of f was 10.35° with 2.31 kPa. The fpeak value was found to be close to the estimated 32°, but there was a significant difference in the fresidual which was approximated to be 30°. This discrepancy could be attributed to several factors such as the accuracy of rainfall data and geometry as well as the absence of some soil samples during the investigation. The cohesion values for peak and residual soil shear strength were considered acceptable because of the smaller values compared to the typical cohesion of SM (Silty Sand) which was set at 20°. Moreover, slope stability analyses conducted using only the effect of tension crack produced a safety factor of 0.996 while those with only the effect of rainfall had 1.172. The results showed that water pressure in tension crack had a more significant influence on slope stability compared to rain. However, it was important to state that the variation in the significance of each factor was based on the assumptions made during the analysis. [ABSTRACT FROM AUTHOR]

In response to the problem of significant post-construction settlement that may occur in a motor racing circuit (MRC), two representative composite foundation testing areas, PHC pile (pre-tensioned spun high-strength concrete pile) and CFG pile (cement fly ash gravel pile), were selected for field tests to obtain the deformation law of pile–soil. Then, finite element numerical simulation was used to carry out back analysis on the geological mechanical parameters of the testing areas. The results showed that the error of soil settlement between the piles in the PHC pile and CFG pile testing areas were 8.2% and 9.6%, respectively, with good inversion precision. The obtained geological mechanical parameters can be used to predict the settlement of the rest of the MRC. On this basis, a finite element numerical model was constructed to analyze the bearing and deformation characteristics of the foundation of the MRC under five types of working conditions that may cause significant post-construction settlement. It showed that the settlement of the embankment was large in the middle and small on both sides after the consolidation of the embankment. The maximum settlement was about 27.0 mm, and the maximum longitudinal uneven settlement ratio of the embankment was 1.3/4000. The axial force of piles in the PHC pile and CFG pile composite foundations increased first and then decreased with depth. The maximum bending moment was located at the foot of slopes or at the boundary of strata, which was relatively small in the middle of the embankment. The deformation of the embankment and the bearing capacity of the piles could meet engineering requirements. This study has certain guiding significance for the design and construction of similar pile-net composite foundations. [ABSTRACT FROM AUTHOR]

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance. [ABSTRACT FROM AUTHOR]

The underground powerhouse of the Shuangjiangkou hydropower station is one of the largest caverns under construction in China, and its stability during construction is crucial for safe construction. To study the stability of the surrounding rock during excavation, the displacement and stress of the surrounding rock were monitored by multi-point displacement meters and bolt stress meters. Based on the monitoring data, the elastic modulus, Poisson’s ratio, friction angle, and cohesion of surrounding rock were inversely analyzed by the PSO-BP algorithm. Then, the back-analyzed parameters were used to simulate the subsequent excavations and predict the stability of surrounding rock during the following construction. The analysis results show that the surrounding rocks were generally stable during the initial four stages of excavation, and the main factors affecting their stability were blasts and unfavorable geological structures, including the lamprophyre vein and the F1 fault. These unfavorable geological structures also significantly decrease the mechanical parameters of surrounding rock as demonstrated by back analysis, and the stability prediction results show that the omnibus bar cave and the tailrace tunnel were at the greatest risk of instability during the subsequent excavations. This study provides a practical analysis for engineering excavation of the underground caverns.

A landslide was experienced in Karyamekar Village, Cilawu District, Garut Regency, West Java, on 12 February 2021 at approximately 300 m length with a depth of 20 m, leading to a steep slope. Therefore, this study aimed to use 3D back analysis to determine soil shear strength to be subsequently applied in analyzing the possibility of further landslide with due consideration for tension crack and rainfall effect. It was also used to understand the influence of these factors on slope stability. Filled tension crack and rainfall effects were modeled using Finite Element Method (FEM) while Limit Equilibrium Method (LEM) was applied for back analysis. The results of back analysis showed that peak shear strength value of φ was 31.18° at a cohesion of 8.01 kPa while the residual shear strength value of φ was 10.35° with 2.31 kPa. The φpeak value was found to be close to the estimated 32°, but there was a significant difference in the φresidual which was approximated to be 30°. This discrepancy could be attributed to several factors such as the accuracy of rainfall data and geometry as well as the absence of some soil samples during the investigation. The cohesion values for peak and residual soil shear strength were considered acceptable because of the smaller values compared to the typical cohesion of SM (Silty Sand) which was set at 20°. Moreover, slope stability analyses conducted using only the effect of tension crack produced a safety factor of 0.996 while those with only the effect of rainfall had 1.172. The results showed that water pressure in tension crack had a more significant influence on slope stability compared to rain. However, it was important to state that the variation in the significance of each factor was based on the assumptions made during the analysis.

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Claystone is potentially the main rock formation for the deep geological disposal of high-level radioactive nuclear waste. A major factor that affects the deformation of the host medium is effective stress. Therefore, studying the effective stress principle of claystone is essential for a stability analysis of waste disposal facilities. Consolidated drained (CD) tests were carried out on claystone samples to study their effective stress principle in this paper. Firstly, two samples were saturated under a specified confining pressure and pore pressure for about one month. Secondly, the confining pressure and pore pressure were increased to a specified value simultaneously and then reverted to the previous stress state (the deformations of the samples were recorded during the whole process). Different incremental combinations of the confining pressure and pore pressure were tried at this step. Finally, the effective stress coefficients of the samples were obtained through a back analysis. Furthermore, some potential influencing factors (the neutral stress and loading rate) of the effective stress coefficient were also studied through additional tests. Some interesting results are worth mentioning: (1) the effective stress coefficient of claystone is close to one; (2) the neutral stress and loading rate may have little effect on the effective stress coefficient of claystone. [ABSTRACT FROM AUTHOR]

The uncertainty of surrounding rock mechanical parameters has much great influence on design, construction and stability evaluation for tunnel engineering. However, the traditional deterministic back analysis cannot consider the uncertainty of surrounding rock parameters. In the paper, we proposed a novel probabilistic back analysis method integrating adaptive momentum (Adam) stochastic optimization algorithm, Bayesian method and multi-output Gaussian process (MOGP)—Adam–Bayesian-based MOGP (ABMOGP), and implemented it in Python. The proposed method adopts MOGP to build the nonlinear mapping relationships between displacements and mechanical parameters of surrounding rock, and utilizes Adam algorithm to optimize the hyperparameters of MOGP model, and employs Bayesian method to deal with the uncertainty of mechanical parameters of surrounding rock. The proposed method was applied to a deep-buried soft-rock highway tunnel. At the tunnel, the uncertainty of elastic modulus, Poisson's ratio, internal friction angle and cohesion of surrounding rock was modeled as random variables. Based on the mechanical parameters of surrounding rock identified by the proposed method, the calculated value of displacement agreed closely with the field measured value of displacement. The results show that the ABMOGP can present the uncertainty of surrounding rock mechanical parameters reasonably, and has great advantages over neural networks and support vector regression method. The proposed method provides an important novel approach for the probabilistic back analysis to determine surrounding rock mechanical parameters. Highlights: A novel probabilistic back analysis method integrating Adam, Bayesian and multi output Gaussian process was proposed. ABMOGP has great advantages over neural networks and support vector regression method. ABMOGP provides an important approach to identify and determine surrounding rock mechanical parameters. [ABSTRACT FROM AUTHOR]

Process-based rockfall simulation models attempt to better emulate rockfall dynamics to different degrees. As no model is perfect, their development is often accompanied and validated by the valuable collection of rockfall databases covering a range of site geometries, rock masses, velocities, and related energies that the models are designed for. Additionally, such rockfall data can serve as a base for assessing the model's sensitivity to different parameters, evaluating their predictability and helping calibrate the model's parameters from back calculation and analyses. As the involved rock volumes/masses increase, the complexity of conducting field-test experiments to build up rockfall databases increases to a point where such experiments become impracticable. To the author's knowledge, none have reconstructed rockfall data in 3D from real events involving block fragments of approximately 500 metric tons. A back analysis of the 2015 Mel de la Niva rockfall event is performed in this paper, contributing to a novel documentation in terms of kinetic energy values, bounce heights, velocities, and 3D lateral deviations of these rare events involving block fragments of approximately 200 m3. Rockfall simulations are then performed on a "per-impact" basis to illustrate how the reconstructed data from the site can be used to validate results from simulation models. [ABSTRACT FROM AUTHOR]

Aiming at some stress-induced failure phenomena in surrounding rock that occur during the construction of super-long and deep-buried tunnels, a method for estimating the in situ stress in the tunnels based on multivariate information integration is proposed, which uses a small amount of in situ stress measurement, stereographic projection technology, and a numerical simulation method. Firstly, by conducting a macroscopic analysis of the regional geological structure, topography, and pre-excavated small tunnels (such as exploration of adits and pilot tunnels), the strength of the tectonic stress field and the orientation of the principal stresses in the tunnel sections are preliminarily determined. Secondly, the reliability of the in situ stress measurement data were analyzed using full-space stereographic projection and the plane stress projection method. Then, some representative measurement points that reflected the distribution characteristics of in situ stress in the project area, on the whole, were determined. Thirdly, the finite difference (FDM) and multiple regression analysis (MRA) methods were used to inverse the in situ stress field in the project area. The proposed method was applied to a super-long and deep-buried tunnel project in Qinling, and the in situ stress distribution characteristics of the tunnel sections at different mileages were obtained. The results show that both the calculated principal stress values and the azimuth angle of the maximum horizontal principal stress are in good agreement with the measured ones, indicating that the method used in this study is reasonable. Finally, the typical surrounding rock failure phenomena encountered during the excavation of the project were investigated, and targeted treatment measures were proposed. The research results can provide references for support design and disaster management of surrounding rock in deep-buried long tunnels. [ABSTRACT FROM AUTHOR]

Mountain road tunnels are prone to water leakage and lining corrosion under the complex geological conditions and corrosive environments, which will reduce the strength of the lining structure until it loses its load-bearing capacity; eventually, the definitive lining will need to be replaced. In this paper, a highway tunnel in a mountainous area in Southwest China is taken as an example. Field investigation found that the tunnel was seriously corroded by sulfate, the strength of the definitive lining decreased, and large-scale cracks and spalling appeared on the surface, so the operator decided to replace the definitive lining by the method of interval replacement. Based on the data obtained from drilling and coring, a numerical model of long-distance replacement of the definitive lining of the damaged tunnel is established. First, the back analysis of the calculation parameters is carried out, and the modified calculation results are compared with the field monitoring results for verification. Then, the deformation trend of the tunnel and the development of the plastic zone during the process of long-distance replacement of the definitive lining are studied. Finally, the construction scheme is optimized. Numerical analysis results show that the replacement of the definitive lining of the tunnel mainly leads to the settlement of the arch crown and the uplift of the inverted arch. The deformation of the tunnel shows two rapid growth stages and two stable stages during the replacement process; after replacement, the deformation of the arch crown and the inverted arch is divided into two buffer zones and one stable zone. In the progress of the replacement of the definitive lining, the plastic zone does not change. Regarding the reinforcement measures, with the increase in the grouting range, the grouting efficiency decreases, and the effect of the temporary steel arch on controlling the overall deformation is not obvious. The length of the replacement of the single section should be determined according to the geological conditions of the replacement section and the monitoring data during construction. The research results can provide a reference for similar projects for the replacement of the definitive lining.

The purpose of this article is to provide an effective approach to evaluate slope stability in real-time in a reservoir area, which is significant for carrying out risk management for landslide disaster prevention in various engineering practices. A comprehensive idea for stability estimation of bank slope under the influence of rainfall or the reservoir water level is presented in this work. Slope stability analysis and back analysis of soil parameters are both included based on numerical simulation. The mechanical parameters of the bank slope were first back-analyzed using particle swarm optimization (PSO), and real-time stability analysis with high accuracy and efficiency was then established based on multiple continuously monitored displacements. Two case studies were carried out in this study. The results show that (1) based on the real-time monitored displacement and numerical simulation, the mechanical parameters of the slope can be reasonably retrieved through PSO; and (2) based on the inverse mechanical parameters, the safety factors of the slope can be numerically obtained, so that the real-time estimation of slope stability can be realized.

Determining the properties of mine waste dump material is very difficult, it is caused by the difference of dimension between the laboratory equipment and field material size distribution. This study aims to determine the material properties from mine waste dump material using back analysis method after failure was occurred. The back analysis method is based on a trial and error concept which focuses on failure geometry and it is conducted by varying the cohesion and friction angle until the safety factor was less than 1.0. The laboratory results showed a 66.2 kPa change as well as a 48.8°friction angle deviation in mine waste dump material properties, while the back analysis indicated 33.7 kPa and 27.4°. It means, there is a reduction in the value. Therefore, the increasing in these properties are expected to allow the redesign of heap stability.

The article discusses the application of statistical metrics for the validation of comprehensive non-linear soil models. The assessment was carried out on the basis of triaxial, oedometer, consolidation and plate load tests of sandy and clay soils. Validation of non-linear soil models was divided depending on the problem type: strength-type problem and strain-type problem. For a strength-type problem the indicators of failure points should be compared. In the course of strain-type problem the stress-strain curves should be compared. Average ratio of experimental data to calculated and coefficient of variation showed the highest efficiency for standard triaxial and oedometer tests, as they allow taking into account the specificity of the deviation and its variability. Other statistical metrics are less effective in geotechnical engineering. Validation according to consolidation tests is recommended to be performed based on the analysis of the time of 100% primary consolidation and the slope of the consolidation curve during the creep phase. In the course of stress paths analysis (for example, in the course of unloading and further reloading), the advantage should be given to visual assessment. Acceptable values of validation metrics for geotechnical engineering are proposed. The specific values of deviations should be determined by the analyst depending on the required accuracy of calculations, the responsibility of the construction object and the assessment of the risk of an accident.

The Moroccan road network is susceptible to multiple landslides annually, particularly in the northern regions due to high rainfall and specific geology. These events result in significant economic and social negative consequences, highlighting the need for sustainable and cost-effective solutions for network maintenance. This study outlines the methodology employed in addressing the issues within the RR410 regional road (Rifain region of Morocco), which entailed a thorough examination of the malfunctions, specific surveys, laboratory testing, and problem modeling. By incorporating long-term test-derived shear strength parameters, the model indicated that the road platform was stable, and back analysis using TALREN 4 software allows for model calibration. At kilometric point 23, using earthwork-based solutions (e.g., purging and replacing the base layer, employing granular water-insensitive substitution material) was found to provide a sustainable alternative to the expensive reinforced concrete-based solutions commonly used. Furthermore, these solutions contributed to the use of environmentally friendly and locally sourced materials. Road alignment rectification to anchor the platform in suitable soil was also an effective solution, as demonstrated at kilometric point 48. Additionally, enhancing the drainage and sanitation infrastructure, such as installing draining trenches, spurs, and reinforcing existing water structures, is a crucial aspect of addressing most landslides in the region. [ABSTRACT FROM AUTHOR]

The determination of soil parameters in geotechnical engineering and their variations during the construction process have long been a focal point for engineering designers. While the artificial neural network (ANN) has been employed for back analysis of soil parameters, its application to caisson sinking processes remains limited. This study focuses on the Nanjing Longtan Yangtze River Bridge project, specifically the south anchoring of an ultra-large rectangular caisson. A comprehensive analysis of the sinking process was conducted using 400 finite element method (FEM) models to obtain the structural stress and earth pressure at key locations. Multiple combinations of soil parameters were considered, resulting in a diverse set of simulation results. These results were then utilized as training samples to develop a back-propagating artificial neural network (BP ANN), which utilized the structural stress and earth pressure as input sets and the soil parameters as output sets. The BP ANN was individually trained for each stage of the sinking process. Subsequently, the trained ANN was employed to predict the soil parameters under different working conditions based on actual monitoring data from engineering projects. The obtained soil parameter variations were further analyzed, leading to the following conclusions: (1) The soil parameters estimated by the ANN exhibited strong agreement with the original values from the geological survey report, validating their reliability; (2) The surrounding soil during the caisson sinking exhibited three distinct states: a stable state prior to the arrival of the cutting edges, a strengthened state upon the arrival of the cutting edges, and a disturbed state after the passage of the cutting edges; (3) In the stable state, the soil parameters closely resembled the original values, whereas in the strengthened state, the soil strength and stiffness significantly increased, while the Poisson's ratio decreased. In the disturbed state, the soil strength and stiffness were slightly lower than the original values. This study represents a valuable exploration of back analysis for caisson engineering. The findings provide important insights for similar engineering design and construction projects. [ABSTRACT FROM AUTHOR]

Loess landslides pose significant physical and environmental threats to communities of the Heifangtai Terrace in Gansu Province, China. Estimating potentially affected areas using dynamic numerical simulations provides a foundation for risk assessment and management. However, current studies often use rheological input parameters derived from the back analysis of an individual historical landslide, which requires that the potential landslide is quite similar to the historical landslide to guarantee the accuracy of predictions. We collected data from 20 loess flowslides that occurred from 2015 to 2019 in the Heifangtai Terrace. A friction model embedded in MassFlow was used to simulate run-out behavior. Accumulation depths at multiple control points were used as observation information. A multi-objective optimization back analysis was used to calibrate optimal rheological parameters for each flowslide, yielding a region-specific database of 20 datasets for statistical analyses. Finally, two flowslides that occurred in 2019 were employed as potential landslides to conduct probabilistic run-out predictions based on the database. These predictions agreed well with the observed accumulation areas in both cases, thus validating our method. This database will be useful for future landslide run-out predictions in this area. [ABSTRACT FROM AUTHOR]

ABSTRACT: Numerical back analysis is a valuable tool available to rock mechanics researchers and practitioners. Recent studies related to back analysis methods focused primarily on applications of increasingly sophisticated optimization algorithms (primarily machine learning algorithms) to rock mechanics problems. These methods have typically been applied to relatively simple problems; however, more complex back analyses continue to be conducted primarily through ad hoc manual trial-and-error processes. This paper provides a review of the basic concepts and recent developments in the field of numerical back analysis for rock mechanics, as well as some discussion of the relationship between back analysis and more broadly established frameworks for numerical modelling. The challenges of flexible constraints, non-uniqueness, material model limitations, and disparate data sources are considered, and representative case studies are presented to illustrate their impacts on back analyses. The role of back analysis (or “model calibration”) in bonded particle modelling (BPM), bonded block modelling (BBM), and synthetic rock mass (SRM) modelling is also considered, and suggestions are made for further studies on this topic.

At present, there is no commonly used method for predicting soil resistance to the driving (SRD) of monopiles, because all available methods are developed based on an installed offshore pile with a diameter of 2–3 m. In addition, due to the complexity of soil profiles in situ, the accuracy of methods used is often not stable under different soil conditions. Based on two typical stratified soil conditions of offshore wind farms in the East China Sea, which are clay-interlayered sand and sand-interlayered clay where, according to the pile driving records of the monopiles in sites, the SRD is obtained by back analysis using the method of the wave equation. At the same time, SRD is also calculated using Steven and Alm methods and compared with that of the back analysis. The results show that the SRDs from the Steven and Alm methods are basically consistent with that of the back analysis, but the predicted SRD of the clay layer is higher than that of back analysis, while the predicted SRD of the sand layer is lower. Based on the characteristics of SRD in different soil layers, a modified method for calculating unit friction in clay and the unit end resistance in sand is proposed for stratified soil, and the error between SRD of the proposed method and of the back analysis was approximately 20%. It could be helpful to improve the accuracy of the monopile drivability analysis in stratified soil. [ABSTRACT FROM AUTHOR]

Landslide risk analysis is a common geotechnical evaluation and it aims to protect life and infrastructure. In the case of sensitive clay zones, landslides can affect large areas and are difficult to predict. Here we propose a methodology to determine the landslide hazard across a large territory, and we apply our approach to the Saint-Jean-Vianney area, Quebec, Canada. The initial step consists of creating a 3D model of the surficial deposits of the target area. After creating a chart of the material electrical resistivity adapted for eastern Canada, we applied electric induction to interpret the regional soil. We transposed parameter values obtained from the laboratory to a larger scale, that is to a regional slope using the results of a back analysis undertaken earlier, on a smaller slide within the same area. The regional 3D model of deposits is then used to develop a zonation map of slopes that are at risk and their respective constraint areas with the study region. This approach allowed us to target specific areas where a more precise stability analysis would be required. Our methodology offers an effective tool for stability analysis in territories characterized by the presence of sensitive clays. [ABSTRACT FROM AUTHOR]

Determining the properties of mine waste dump material is very difficult, it is caused by the difference of dimension between the laboratory equipment and field material size distribution. This study aims to determine the material properties from mine waste dump material using back analysis method after failure was occurred. The back analysis method is based on a trial and error concept which focuses on failure geometry and it is conducted by varying the cohesion and friction angle until the safety factor was less than 1.0. The laboratory results showed a 66.2 kPa change as well as a 48.8°friction angle deviation in mine waste dump material properties, while the back analysis indicated 33.7 kPa and 27.4°. It means, there is a reduction in the value. Therefore, the increasing in these properties are expected to allow the redesign of heap stability. [ABSTRACT FROM AUTHOR]

In response to the problem of significant post-construction settlement that may occur in a motor racing circuit (MRC), two representative composite foundation testing areas, PHC pile (pre-tensioned spun high-strength concrete pile) and CFG pile (cement fly ash gravel pile), were selected for field tests to obtain the deformation law of pile–soil. Then, finite element numerical simulation was used to carry out back analysis on the geological mechanical parameters of the testing areas. The results showed that the error of soil settlement between the piles in the PHC pile and CFG pile testing areas were 8.2% and 9.6%, respectively, with good inversion precision. The obtained geological mechanical parameters can be used to predict the settlement of the rest of the MRC. On this basis, a finite element numerical model was constructed to analyze the bearing and deformation characteristics of the foundation of the MRC under five types of working conditions that may cause significant post-construction settlement. It showed that the settlement of the embankment was large in the middle and small on both sides after the consolidation of the embankment. The maximum settlement was about 27.0 mm, and the maximum longitudinal uneven settlement ratio of the embankment was 1.3/4000. The axial force of piles in the PHC pile and CFG pile composite foundations increased first and then decreased with depth. The maximum bending moment was located at the foot of slopes or at the boundary of strata, which was relatively small in the middle of the embankment. The deformation of the embankment and the bearing capacity of the piles could meet engineering requirements. This study has certain guiding significance for the design and construction of similar pile-net composite foundations.

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance.

Municipal sludge is a sedimentation waste produced during the wastewater process in sewage treatment plants. Among recent studies, pilot and field tests showed that chemical conditioning combined with vacuum preloading can effectively treat municipal sludge. To further understand the drainage and consolidation characteristics of the conditioning sludge during vacuum preloading, a large deformation nonlinear numerical simulation model based on the equal strain condition was developed to simulate and analyze the pilot and field tests, whereas the simulation results were not satisfactory. The results of the numerical analysis of the pilot test showed that the predicted consolidation degree was greater than that measured by the field tests, which is attributed to the relatively low permeability layer formed during the preloading process of the prefabricated vertical drain. To better reflect the consolidation process of the conditioned sludge, a simplified analysis method considering the low permeability layer around the prefabricated vertical drain was proposed. The initial permeability coefficient of the low permeability layer is determined via numerical simulations using finite difference method. The predicted settlement curve was in good agreement with the measured results, which indicated that the numerical simulation based on the equal strain condition considering the relatively low permeability layer can better analyze the consolidation process of ferric chloride-conditioning sludge with vacuum preloading. [ABSTRACT FROM AUTHOR]