Several giant water diversion projects go through large expansive soil areas in China. It is challenging to ensure the slope stability of the deep excavated expansive soil canal segments due to its undesirable geologic, and physical and mechanical properties. This study develops a novel deformation prediction method for deep excavated expansive soil canal slopes based on the eXtreme Gradient Boosting (XGBoost) algorithm, and provides a result interpretation using SHAP (SHapley Additive exPlanations). Firstly, the main factors influencing the slope deformation of deep excavated expansive soil canal segments are discussed. Subsequently, the integrated XGBoost-SHAP method is proposed for the prediction model construction of slope deformation and its result interpretation, the related the methodologies including the variational mode decomposition (VMD), XGBoost, and SHAP are also presented. The ten-fold cross-validation is employed to find the hyperparameter combination for the deformation prediction models, and the model performances are compared with common validation measurements. The effectiveness of the explanatory deformation prediction method is verified through one typical deep excavated expansive canal segment of the mid-route of the South-to-North Water Diversion Project in China. The case study shows that the VMD algorithm can well decompose the trend, periodic and fluctuating displacements. Compared with random forest and least squares-support vector machines, the XGBoost-algorithm based model achieves the better prediction performance. SHAP provides prediction result interpretations at both global and local levels. Time dependent effect and groundwater have a significant impact on the slope deformation of deep excavated expansive soil canal segments. The integrated SHAP-XGBoost method can provide a reference for predicting slope deformation of the similar projects. It can also quantify the contribution and mechanism of influencing factors on slope deformation at both global and local levels, which is conducive to implement reinforcement measures.

Concrete is a multi-phase, multi-component, heterogeneous composite material and a typical porous medium with a complex microstructure. Under freeze-thaw cycles (FTCs), pressures from the freezing and migration of water within the pores of the concrete cause freeze-thaw damage, which is influenced by its microstructure. To effectively simulate and understand the freeze-thaw damage mechanism, it is crucial to use a geometric structure that reflects the actual pore distribution. This study employs the μic model to create a geometric structure of hardened cement paste, capturing the spatial distribution of pores. The study utilizes the peridynamics (PD) method based on ice formation and crystallization thermodynamics to investigate the freeze-thaw damage evolution and the deterioration of mechanical properties in cement paste under FTCs. The simulations revealed the entire freeze-thaw damage process of cement paste, including internal crack initiation and propagation, as well as surface freeze-thaw spalling. The freeze-thaw damage of cement paste intensifies with increasing FTCs, and the extension of cracks diminishes the interconnection among the skeletons constituting the cement paste microstructure, leading to decreased tensile strength and elastic modulus. Additionally, the stress-strain evolution behavior of freeze-thaw cement paste under uniaxial tensile loads was obtained. The tensile strength and elastic modulus of cement paste decrease with increasing FTCs, and the higher the water-cement (w/c) ratio, the greater the reduction in strength and modulus.

Deformation, as the most intuitive index, can reflect the operation status of hydraulic structures comprehensively, and reasonable analysis of deformation behavior has important guiding significance for structural long-term service. Currently, the health evaluation of dam deformation behavior has attracted widespread attention and extensive research from scholars due to its great importance. However, given that the sluice is a low-head hydraulic structure, the consequences of its failure are easily overlooked without sufficient attention. While the influencing factors of the sluice’s deformation are almost identical to those of a concrete dam, nonuniform deformation is the key issue in the sluice’s case because of the uneven property of the external load and soil foundation, and referencing the traditional deformation statistical model of a concrete dam cannot directly represent the nonuniform deformation behavior of a sluice. In this paper, we assume that the deformation at various positions of the sluice consist of both overall and individual effects, where overall effect values describe the deformation response trend of the sluice structure under external loads, and individual effect values represent the degree to which the deformation of a single point deviates from the overall deformation. Then, the random coefficient model of panel data is introduced into the analysis of sluice deformation to handle the unobservable overall and individual effects. Furthermore, the maximum entropy principle is applied, both to approximate the probability distribution function of individual effect extreme values and to determine the early warning indicators, completing the assessment and analysis of the nonuniform deformation state. Finally, taking a project as an example, we show that the method proposed can effectively identify the overall deformation trend of the sluice and the deviation degree of each measuring point from the overall deformation, which provides a novel approach for sluice deformation behavior research.

Taking a 4 km long deeply excavated expansive soil canal segment of the South-North Water Transfer Project Middle Route (MR-SNWTP) in China as an example, a comprehensive investigation of the deformation mechanism and effect of treatment work on the slope with high groundwater level was carried out. First, the project background and initial behaviour were introduced. Second, a series of site investigation, including visual inspection, exploration pit, and geological penetrating radar, was employed to identify the deformation characteristics, appearance damage, and groundwater distribution. Third, the environmental factors were analyzed, and the spatio-temporal deformation characteristics were identified. Then the zoned numerical model was built to analyze the slope stability. Finally, the deformation mechanism and effect of treatment work were discussed. The results show that although the slope surface was replaced with a cement-treated weak expansive soil layer, the water vapor exchange between the undisturbed expansive soils and atmosphere is not completely isolated. The perched groundwater is also still replenished by precipitation infiltration. Groundwater, perched groundwater, long-large fissure and dense fissure zone induce the lateral creep deformation of the slope, and result in significant trend changes. The stability of the deformed body is in a critical state. The critical sliding surface is a broken line consisting of a gentle dip angle at the leading edge and a steep dip angle at the trailing edge. The depth of the potential sliding surface varies due to the geological condition and arrangement of anti-slide piles. Drainage wells can discharge deeper groundwater, thus can lower groundwater level and discharge perched groundwater. Supporting and unloading measures should also be taken to improve the slope stability.

The previous zonal deformation prediction models for super-high arch dams focus on the measured deformation law, which cannot capture the trend, periodic and fluctuating characteristics. This greatly limits their physical interpretation and practical applications. To address this issue, this paper proposes an optimized zonal deformation prediction model. First, the variational mode decomposition algorithm is applied to split displacement into trend, periodic and fluctuating components. Representative environmental and load factors are determined by the hierarchical clustering on principal components, and then also decomposed into the trend, low- and high-frequency components according to their physical meanings. Second, an optimized dynamic time warping (DTW) based on shape-based distance is employed to divide the displacement components of measured points into different zones. The centroid sequences are calculated to capture the shared characteristics of the corresponding deformation zones. The zonal data sets of the centroid sequences of displacement components and their strongly related influencing factor components are established. Third, the optimized zonal deformation prediction models using three machine learning algorithms (random forest, least squares support vector machines, and boosted regression tree) and the improved hydrostatic-thermal-time model are constructed. The effectiveness of the optimized model is verified using the measured data collected from the Xiluodu dam. The case study shows that the optimized model can well explain the spatio-temporal characteristics of the trend, periodic and fluctuating displacements. Affected by the geological condition, the radial displacement distribution is not completely symmetrical under high reservoir water level.

Structural health diagnosis of expansive soil slopes requires timely analysis of deformation monitoring data. A method for spatiotemporal clustering of monitoring data for health diagnosis is proposed. First, the deformation time series is upgraded to a panel time series, which includes spatial positions and temporal variations, and similarity characteristics of spatiotemporal deformation are discussed. Second, a similarity distance indicator is defined using three deformation variables: weighted absolute distance, weighted increment distance, and weighted growth rate distance. Third, a spatiotemporal clustering model of the deformation of expansive soil slope based on a spectral clustering algorithm is developed, together with a scoring algorithm for determining optimal clusters. The method analyses and diagnoses the deformation behaviour of the expansive soil slope structure of China's South-to-North Water Diversion Project central line. The advantage of the proposed method is demonstrated by comparing its results with results obtained by the commonly used temporal clustering method. It is further shown how the new method can be used to identify abnormal regions of expansive soil slope deformation.

Prediction of the macroscopic mechanical properties and damage evolution of cement paste is significant; however, it poses challenges due to the complex multiphase heterogeneity and porosity of the hydration microstructure. This study presents a robust implicit-explicit hybrid computational framework based on the bond-based peridynamics for predicting crack evolution and macroscopic mechanical properties of porous quasi -brittle materials. Specifically, the prototype micro-elastic brittle (PMB) material model is improved by consid-ering an attenuation kernel function and surface effect correction. Subsequently, an efficient implicit-explicit hybrid approach is developed for the solution of the strong form of peridynamic equation of motion. A crit-ical damage index of the global system is specified to switch the solver from implicit to explicit. CEMHYD3D software is employed to generate the microstructure of hydration under uniaxial tension. Systematic simulations of the microstructure explain the influence of several computational strategies on the results, i.e., the approach of boundary enforcement, the geometry of porous microstructure, the loading rate in the explicit stage, and breaking the bonds across the voids or not. The simulations demonstrate that applying the improved bond-based peridynamic solver on hydration microstructures can capture their cracking behavior and macroscopic me-chanical properties, e.g., stress-strain curve, peak stress, maximum tensile strain, Young's modulus, and fracture energy release rate. This study also paves the way for peridynamic multiscale modeling of cement-based materials.

The Digital Image Correlation technique recorded the process of self-compacting rubberized concrete (SCRC) under a pre-notched three-point bending beam. Load-crack opening displacement curves were measured, and the fracture process zone and crack opening displacement were comparatively analyzed to investigate the effect of rubber content and notch depth. The test results show that the incorporation of rubber increases the fracture toughness and fracture energy of the self-compacting concrete, and the increased amount is related to the incorporation amount of rubber. The fracture pattern of the specimen has also changed due to the incorporation of rubber. Finally, a mesoscopic model was proposed and verified by comparing the numerical simulation and test results.

Deeply excavated expansive soil canal slopes can have instability risks during operations. The deformation mechanism is analyzed using internal and external factors, such as engineering geology, hydrogeology, rainfall, and groundwater levels in the middle expansive soil canal slope in the central line of the South-to-North Water Diversion Project, Taocha District, Nanyang City, Henan Province, China. The stability of the canal slope and the effect of the reinforcement and disposal measures are evaluated with a geological survey, manual inspection of the defects, safety monitoring data analysis, hidden danger geophysical detection, and stability numerical simulation. The following conclusions are drawn: (1) the surface-refilled cement-treated soil failed to isolate the water vapor exchange between the expansive soil and the atmosphere; and (2) the stagnant water in the upper layer is replenished by precipitation, leading to the increase in the groundwater level during the rainy season and the decrease in the groundwater level during the dry season. Shear creep deformation occurs along the fissure of the second to fourth grade canal slopes. The potential sliding surface consists of a gentle dip angle leading edge and a steep dip angle trailing edge. The arrangement of the antisliding piles on the water-passing section and the crack surface distribution had an effect, and the potential shear outlet is in the first grade berm, the deformed body is still developing, and the trailing edge is not apparent. The potential failure mode of the slope is deep sliding, and the current safety factor is 1.002. A combination of groundwater discharge and micropile reinforcement is required to improve the stability of the canal slope. After the drainage and reinforcement are implemented, the slope's deep antislide stability safety factor increases to 1.462, which met the safety requirements. The results could provide a reference for the operation management and reinforcement governance of similar projects.

The first-order second-moment reliability method (FORM) is a prevalent method in the reliability analyzing community owing to its accuracy and efficiency. However, it may encounter unstable solutions such as periodic oscillation and non-convergence when a high-dimensional problem with complex performance functions is involved. In this study, a novel adaptive approach based on over-distance interval searching and precise step size determining is presented to work out a solution for the above issues. First, the relationship between the state of the iterative rotation angle and the ideal step size was noticed. Therefore, a rotation control condition is proposed to dictate the selection of step size for the algorithm. After that, the determination of the step size of the proposed method here can be realized in two stages: In the first stage, an over-distance step size interval searching approach is established to define an initial exploration interval of step size with a length of 1. In the second stage, a determination approach of adaptive precise step size is then proposed to modify the fixed step sizes to make it adaptable to changes, therefore, get the final step sizes for the proposed method. Together, an over-distance adaptive precise searching approach of step size that can generate self-adaptive step sizes both greater and less than 1 is performed by interplaying the above two stages, thus preventing an excessive amount of redundant workload and speeding up the convergence while reducing the risk of oscillations. Besides, an iterative direction modification approach is given to orient the first iteration more accurately. In addition to that, the proposed method is tested on a offshore jacket platform and other seven examples to verify its performance. The results demonstrate that the proposed method provides excellent robustness and efficiency for both low and high-nonlinearity problems, showing significant merits.

Slope failures in deep excavated expansive soil canals are common when there is a high groundwater level and continuous rainfall. Predicting displacements plays a crucial role in preventing and mitigating such failures. To study this issue, a section of a well-known canal in the Nanyang Basin, part of the South-to-North Water Transfer Project (SNWTP), was examined. This canal section is characterized by deep excavation and expansive soil, and it experiences a high groundwater level. To understand the relationship between groundwater level, rainfall, and slope displacement, a qualitative analysis based on time series data was conducted. An integrated approach for displacement prediction was proposed, which combines the variational mode decomposition (VDM) algorithm, the least squares support vector machine (LSSVM), and k-fold cross-validation. The VDM algorithm decomposed the accumulated displacement into a trend, periodic, and fluctuation component. It also decomposed the environmental factors such as groundwater level, rainfall, canal water level, and air temperature into high-frequency and low-frequency factors. The gray relational analysis was used to determine the correlation between the environmental factors and displacement. For predicting the periodic and fluctuating displacement components, the periodic and fluctuating components of the environmental factors were selected as input datasets for the LSSVM algorithm. Finally, the total displacement was obtained by combining the three predictive components. The results of the study demonstrated that the proposed model achieved satisfactory prediction accuracy. Therefore, it can be considered effective and practical for expansive soil slope engineering with high groundwater conditions.

Acoustic emission (AE) is a useful method for recording fracture processes in concrete. In this work, AE data are recorded during three-point bending tests to fracture of hydraulic concrete. First, AE data are used to analyze concrete's damage development using hits distribution, b-value, Ib-value, and average frequency versus RA value. Second, clustering analysis of AE signals is performed by hierarchical clustering. Third, a support vector machine model based on the gray wolf optimization algorithm is proposed to quantify the degree of damage. Via b-value analysis it is shown that the fracture process of hydraulic concrete can be divided into three stages: microcracks nucleation; microcracks coalescence into macrocracks (macrocrack nucleation); and macrocrack propagation. Further, it is shown that rise time, ringdown counts, energy, duration, amplitude, and central frequency can be used to characterize the failure modes. Specifically, it is found that microcrack nucleation stage is dominated by tensile failures, macrocrack nucleation stage is characterized by rapid increase of shear failures, which become dominant over tensile failures, and macrocrack propagation stage is dominated by shear failures. Via hierarchical cluster analysis, it is found that the fracture process can be divided into three clusters, which corresponds to the three stages obtained via b-value analysis. Finally, the proposed support vector machine model based on gray wolf optimization is found to predict the degree of damage in excellent agreement with experiment. This offers an effective practical method for damage assessment by combining AE with machine learning.

This paper uses the improved bond-based peridynamics (BB PD) method to predict the crack evolution and macroscopic mechanical properties of the hardened cement paste at the micro-scale. Specifically, the prototype micro-elastic brittle (PMB) model is improved by considering an attenuation kernel function and the surface effect correction. Subsequently, the uniaxial tension simulations of hardened cement microstructures generated by mu ic software are systemically conducted to investigate the influences of the heterogeneity of microstructure, size effect, and water-cement ratio (w/c) on crack growth and mechanical properties, i.e., tensile strength and Young's modulus. The results show that the heterogeneity of the microstructure inevitably will cause the discreteness of the macroscopic properties of the numerical cement paste. Therefore, a statistical method is considered to analyze the mechanical properties of the hardened cement. Moreover, the tensile strength has a remarkable size effect, with the size increase of microstructure, the tensile strength decreases; the simulation results fit well with the existing analytical size effect model, i.e., Weibull size effect model, multifractal scaling law, and Ba.zant's size effect law. Besides, the higher ratio of w/c is likely to result in lower tensile strength, Young's modulus and premature damage to the cement paste. The predicted macroscopic mechanical properties are in good agreement with the previous simulation and experimental data, which demonstrates the reliability of the improved bond-based peridynamic method in studying the crack initiation and propagation of hardened cement paste.

Using meso-structural representations of concrete, with aggregates dispersed in mortar, has become a standard approach for damage and fracture analysis. However, there is no full agreement on appropriate modelling of different phases and on the inclusion of interfacial transition zones (ITZ). This work explores different mortar and ITZ formulations and by comparison with own experiments demonstrates that the optimal strategy balancing physical realism and computational efficiency requires: (1) damage-plasticity formulation for mortar, calibrated with mortar tension and compression experiments; (2) cohesive-zone formulation for ITZ with zero-thickness cohesive elements, calibrated with concrete tension and com-pression experiments. Models omitting ITZ are shown to be in poor agreement with experiments, both qualitatively and quantitatively. Models with finite thickness ITZ are also in poorer agreement with experiments compared to those with zero-thickness, despite higher computational complexity. It is rec-ommended that concrete analyses follow the proposed strategy for meso-structure modelling and con-stituents' calibration. (c) 2021 Elsevier Ltd. All rights reserved.

Damage and failure of rubberized self-compacting concrete (RSCC) under uniaxial tension are investigated by acoustic emission (AE) and digital image correlation (DIC) techniques. Four RSCC mixtures containing fine rubber particles with 0%, 5%, 10%, and 15% volume fractions are tested. The effect of rubber content on the macroscopic mechanical behavior, the AE parameters, the strain fields, and the damage developments are analyzed. It is demonstrated that the combined use of AE parameters and DIC strain maps provides an accurate estimation of different stages of damage evolution and that the crack propagation measured by DIC correlates strongly with all AE parameters. Modes of cracking are determined by analyses of average frequency (AF) versus rise time-amplitude (RA) values to demonstrate an additional feature of AE, which can be used for explanations of different behaviors under different loading conditions. It is shown that the substitution of fine aggregates with fine rubber particles leads to a reduction of stiffness, strength, and fracture toughness when the material experiences uniaxial tension. This has to be considered in the design of structures with RSCC.

Sulfate attack is one of the crucial causes for the structural performance degradation of reinforced concrete infrastructures. Herein, a comprehensive multiphase mesoscopic numerical model is proposed to systematically study the chemical reaction-diffusion-mechanical mechanism of concrete under sulfate attack. Unlike existing models, the leaching of solid-phase calcium and the dissolution of solid-phase aluminate are modeled simultaneously in the developed model by introducing dissolution equilibrium equations. Additionally, a calibrated time-dependent model of sulfate concentration is suggested as the boundary condition. The reliability of the proposed model is verified by the third-party experiments from multiple perspectives. Further investigations reveal that the sulfate attack ability is underestimated if the solid-phase calcium leaching is ignored, and the concrete expansion rate is overestimated if the dissolution of solid-phase aluminate is not modeled in the simulation. More importantly, the sulfate attack ability and the concrete expansion rate is overestimated if the time-dependent boundary of sulfate concentration is not taken into consideration. Besides, the sulfate ion diffusion trajectories validate the promoting effect of interface transition zone on the sulfate ion diffusion. The research of this paper provides a theoretical support for the durability design of concrete under sulfate attack.

以神华准能办公大楼地下室上浮整治为工程背景,通过现场监测、试验等手段对办公楼地下室上浮变形特征、整治设计参数进行分析,提出泄水降压+抗浮锚杆综合整治的工程措施,详细介绍了抗浮整治过程.该地下室上浮属施工控制不当导致力学条件失衡,从而引起结构构件工后失效,变形特征主要表现为地下室梁、柱节点处产生水平裂缝、墙体产生45°斜向裂缝,柱体节点产生明显压曲现象.此类工程仅仅依靠泄水降压并不能够彻底地整治,需结合抗浮锚杆等主动措施.抗浮设计时通过设置监测井确定抗浮水位、采用抗拔试验确定锚固体与土层的粘结强度标准值,保证设计参数的合理性.对于类似地下室上浮工程,采用泄水降压+抗浮锚杆措施,工艺简单,经济性好,整治效果显著.

Anomaly recognition and early warning of monitoring data are of great significance in the field of modern dam safety management. Multidimensional least-squares regression model with the Pauta criterion is a well-known traditional method, but it is easy to misjudge the normal value and miss the outliers. Thereby, an online robust recognition and early warning model combining robust statistics and confidence interval is proposed to detect outliers. The threshold 3 S T + D is set based on the derived confidence interval D and the scale estimator S T (derived from the location M-estimator). Monitoring data obtained from a gravity dam and a rockfill dam were taken as examples to demonstrate the robust recognition and early warning model. The results show that the proposed method can effectively improve the reliability of anomaly recognition and early warnings, which is valuable in engineering applications.

The mechanism of dam safety monitoring model is analyzed; for the dam system comprehensive affected by multi-factor, the mapping relationship between the influence factors and the dam behavior effects domain is usually nonlinear. Synthesizing each kind of factor, 27 parameters are chosen as the main factors which affect the accuracy of the monitoring model. Taking the actual monitoring data as the evaluation factor, the dam safety monitoring model based on the random forest (RF) intelligent algorithm was built with the actual monitoring data to predict uplift pressure. At the same time, test the significance of each variable based on the RF monitoring model and calculate the importance degree of each variable for the model through the importance function. It is indicated that RF model can be relatively fast and accurately predict the uplift pressure of the dam according to the influence factors. The average prediction accuracy is more than 95%. As compared with other intelligent algorithms such as support vector machine, RF has better robustness, higher prediction accuracy, and faster convergence speed. Because of the uniformity of the calculation procedure and the universality of the prediction method, the RF model also has reasonable extrapolation for other dam safety monitoring models (such as crack opening and seepage discharge). Significance test results obtained by the two methods have shown that the impact of reservoir water level and daily rainfall on the uplift pressure is significant, and other factors’ impact on dam deformation is unstable and changes with the external environmental influence.

Rubberized concrete is a new type of building material intended to ultilise waste rubber with a potential for significant economic and environmental benefits. However, its strength is lower than the strength of ordinary concrete due to the introduction of rubber material, which might affect its application in practical engineering. To improve the mechanical performance of rubberized self-compacting concrete (RSCC), it is a necessary to study the internal mechanisms of strength formation, degradation and failure. Based on the uniaxial tensile test of RSCC, this work reports on the development and validation of a mesoscale model of RSCC, which accounts for its heterogeneity. RSCC is considered to be composed of mortar, coarse aggregate, rubber particles, aggregate-mortar interface transition zone (A-M ITZ), rubber particle-mortar interface transition zone (R-M ITZ), and initial defects. The mesoscopic model is validated by comparing the simulation results with test results. The model is then used to analyse the mechanical properties, crack generation and propagation, and expansion of self-compacting concrete (SCC) and RSCC are compared and analysed. Further, the effects of different volume fractions of rubber on the mechanical properties of RSCC are studied. It is found that the mechanical properties and final fracture surface morphology of RSCC with different rubber content are significantly different, and the causes of these differences are discussed.