Anhui and Huaihe River Institute of Hydraulic Research

Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT) preprocess and support vector machine (SVM) was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN), regular SVM, and wavelet preprocessed artificial neural networks (WANN) models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE), Pearson correlation coefficient (r), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

Abstract. Flooding represents one of the most severe natural disasters threatening the development of human society. A model that is capable of predicting the hydrological responses in watershed with management practices during flood period would be a crucial tool for pre-assessment of flood reduction measures. The Soil and Water Assessment Tool (SWAT) is a semi-distributed hydrological model that is well capable of runoff and water quality modeling under changed scenarios. The original SWAT model is a long-term yield model. However, a daily simulation time step and a continuous time marching limit the application of the SWAT model for detailed, event-based flood simulation. In addition, SWAT uses a basin level parameter that is fixed for the whole catchment to parameterize the unit hydrograph (UH), thereby ignoring the spatial heterogeneity among the sub-basins when adjusting the shape of the UHs. This paper developed a method to perform event-based flood simulation on a sub-daily timescale based on SWAT2005 and simultaneously improved the UH method used in the original SWAT model. First, model programs for surface runoff and water routing were modified to a sub-daily timescale. Subsequently, the entire loop structure was broken into discrete flood events in order to obtain a SWAT-EVENT model in which antecedent soil moisture and antecedent reach storage could be obtained from daily simulations of the original SWAT model. Finally, the original lumped UH parameter was refined into a set of distributed ones to reflect the spatial variability of the studied area. The modified SWAT-EVENT model was used in the Wangjiaba catchment located in the upper reaches of the Huaihe River in China. Daily calibration and validation procedures were first performed for the SWAT model with long-term flow data from 1990 to 2010, after which sub-daily (Δt=2 h) calibration and validation in the SWAT-EVENT model were conducted with 24 flood events originating primarily during the flood seasons within the same time span. Daily simulation results demonstrated that the SWAT model could yield very good performances in reproducing streamflow for both whole year and flood period. Event-based flood simulation results simulated by the sub-daily SWAT-EVENT model indicated reliable performances, with ENS values varying from 0.67 to 0.95. The SWAT-EVENT model, compared to the SWAT model, particularly improved the simulation accuracies of the flood peaks. Furthermore, the SWAT-EVENT model results of the two UH parameterization methods indicated that the use of the distributed parameters resulted in a more reasonable UH characterization and better model fit compared to the lumped UH parameter.

Although the subject of mining and its environmental impacts are very wide to be covered in this review, concerns about the impact of phosphate mining and processing typically emphasis on its potential effects on water pollution, air pollution, and human health were accessed. We reviewed published information at different stages of mining; current mines, closed old mines and reclaimed mines and at different complexity of mining; surface mining, underground mining and sea-bed phosphorite mining. Information was analyzed to understand the association of toxic metals and radioactive elements in the phosphate rocks and to trace the transfer pathways of toxic metals and radioactive elements from the phosphate rocks to the environment. According to the reviewed results the major environmental impacts of phosphate mining and processing on the water resources were: impacts on the hydrology by phosphate industry water usage and landscape changes, and impacts on water quality by discharges of industry wastewater into the waterways. Dust was a common air quality problem throughout all mining activities; fluoride emissions and radon gas emission were also serious problems. Toxic metals and radioactive elements of significant human health problems were Pb, Cd, Hg, Cr, As, U Th and Ra. Most researches agreed that 226 Ra is considered as one of the most toxic radionuclide. The nuclide is of further importance as the parent nuclide of the gaseous 222 Rn which, along with its solid decay products, constitutes a significant source of radiation exposure. Scientific researches on mine water drainage and phosphate mining relationship may help to understand the environmental impacts associated with water resource and water quality.

Urban rivers are complex ecosystems that directly determine the living environment of human beings. Monitoring the urban river water quality indexes is a challenge in water quality evaluation. The purpose of this study was to propose a multi-source remote sensing water quality inversion method based on a small number of samples to solve the problem of scale inconsistency among multi-source remote sensing data, so as to achieve large-scale and efficient inversion of urban river water quality. Since there is a very important problem that the complex nonlinear relationships must be solved between simple ground point data and remote sensing data in water quality inversion, a novel self-optimizing machine learning monitoring method is proposed, which can automatically find the optimal parameters of the model from a small number of samples, and reduce the training time. Meanwhile, in order to strengthen the correlation between water quality parameters and remote sensing data, the feature enhancement method was used for generating the input data. Moreover, to solve the problem of the multi-source data quantity and quality, the spatial mapping method was used to achieve consistency in the water quality information since these data have different nonlinear characteristics. The experimental results show that for unmanned aerial vehicle (UAV) images, the R2 of chlorophyll a (Chla), turbidity (TUB), and ammonia nitrogen (NH3-N) can reached 0.917, 0.877 and 0.846, respectively. Using a satellite image, the R2 of Chla, TUB, and NH3-N can reach 0.827, 0.679 and 0.779, respectively. This method provides a new way to realize the integration of air-space-ground monitoring of urban inland rivers in the future.

Abstract. This study provides a new approach to estimate both anthropogenic non-point-source and point-source nitrogen (N) inputs to the landscape, and determines their impacts on riverine ammonia–nitrogen (AN) flux, providing a foundation for further exploration of anthropogenic effects on N pollution. Our study site is Huai River basin of China, a water–shed with one of the highest levels of N input in the world. Multi-year average (2003–2010) inputs of N to the watershed are 27 200 ± 1100 kg N km−2 yr−1. Non-point sources comprised about 98 % of total N input, and only 2 % of inputs are directly added to the aquatic ecosystem as point sources. Fertilizer application was the largest non-point source of new N to the Huai River basin (69 % of net anthropogenic N inputs), followed by atmospheric deposition (20 %), N fixation in croplands (7 %), and N content of imported food and feed (2 %). High N inputs showed impacts on riverine AN flux: fertilizer application, point-source N input, and atmospheric N deposition were proved as more direct sources to riverine AN flux. Modes of N delivery and losses associated with biological denitrification in rivers, water consumption, interception by dams may influence the extent of export of riverine AN flux from N sources. Our findings highlight the importance of anthropogenic N inputs from both point sources and non-point sources in heavily polluted watersheds, and provide some implications for AN prediction and management.

Abstract Drought monitoring is critical for early warning of drought hazard. This study attempted to develop an integrated remote sensing drought monitoring index (IRSDI), based on meteorological data for 2003–2013 from 40 meteorological stations and soil moisture data from 16 observatory stations, as well as Moderate Resolution Imaging Spectroradiometer data using a linear trend detection method, and standardized precipitation evapotranspiration index. The objective was to investigate drought conditions across the Huai River basin in both space and time. Results indicate that (1) the proposed IRSDI monitors and describes drought conditions across the Huai River basin reasonably well in both space and time; (2) frequency of drought and severe drought are observed during April–May and July–September. The northeastern and eastern parts of Huai River basin are dominated by frequent droughts and intensified drought events. These regions are dominated by dry croplands, grasslands, and highly dense population and are hence more sensitive to drought hazards; (3) intensified droughts are detected during almost all months except January, August, October, and December. Besides, significant intensification of droughts is discerned mainly in eastern and western Huai River basin. The duration and regions dominated by intensified drought events would be a challenge for water resources management in view of agricultural and other activities in these regions in a changing climate.

Reinforced concrete structures strengthened by fiber-reinforced polymer (FRP) always suffer dynamic loadings. Furthermore, the dynamic loadings are always added at the base of static loadings. The success of this strengthening method relies on the effectiveness of the bond of the FRP sheet to the concrete. Although numerous experimental studies have investigated this bond, experimental data concerning dynamic tests on basalt FRP (BFRP) sheets applied on concrete specimens under different initial static loadings are still lacking. This paper presents an experimental investigation on the dynamic bond behavior between the BFRP sheet and concrete under different initial static loadings (0, 30, 50, 80, and 100%) and displacement rate of 70 mm/s. Double-lap shear specimens were used for the tests. The results of the dynamic tests are reported and discussed to evaluate and compare the influence of initial static loading on the dynamic bond behavior between BFRP sheets and concrete. A nonlinear bond stress-slip relationship of the BFRP-concrete interface under different initial static loadings is determined based on an analysis of displacement data, which comprise four empirical parameters, namely, dynamic maximum bond stress τmaxid under different initial static loadings, corresponding slip s0, curve characteristic constant n, and local slip s. The test results show that (1) the dynamic bond capacity of the BFRP-concrete interface decreases with increasing initial static loading; (2) the failure mode of all specimens is debonding in the concrete layer; (3) the dynamic effective bond length of the BFRP-concrete interface increases with increasing initial static loading; and (4) the dynamic maximum bond stress decreases with increasing initial static loading. The calculation models of dynamic bond capacity and dynamic effective bond length considering the influence of initial static loading are also presented.

The poor growth of understory vegetation and the severe losses of soil and water in Pinus massoniana forests have recently become serious concerns in an area in southern China with eroded red soil. The influence of topography on the spatial distribution of vegetation, however, has received little attention. This study combined several multivariate analyses to discern the complicated relationship between understory vegetation and topography. Thirty-six plots (10 m×10 m) were sampled in a field survey of the vegetation and topography in the central red-soil region. The distributions of the understory vegetation differed significantly amongst the topographies. Most plants grew in gullies, and few grew on ridges. The low coverage (25.2%) and number of species (5 per plot) of the vegetation on ridges was due to serious soil erosion. Surface curvature and slope aspect were the first and second most important topographic factors, respectively, affecting the distribution of the vegetation. The relationship between topography and distribution could be described by a linear model. Surface curvature or slope aspect alone, however, could only explain 22.2–59.2% of the variance in distribution. The adaptation of vegetation to specific topographies should be considered for restorations of P. massoniana forests in the study area. The results of this study will be helpful for selecting potential sites for seeding and vegetation restoration to improve the ecology of the study area. Further studies will be needed to identify the mechanism of the distribution of the understory vegetation in these P. massoniana forests.

The purpose of this paper is to clarify the compensation or reduction effect of drought–flood abrupt alternation (DFAA) events on the yield and yield components of rice ( Oryza sativa L.). The experiments, conducted in 2016 and 2017, were on three different drought and flood treatments and compared with normal irrigation conditions. Compared with the normal control group, the average yield reduction of the DFAA groups was 12.98% in 2016 and 29.94% in 2017, and the combination of heavy droughts and heavy flooding was the most unfavorable for yield. The reduction in grains per panicle and the total grain number was the main reason for yield reduction under DFAA stress. The damage in terms of the total grain number amounted to 18.84 and 17.82% in 2016 and 2017, respectively. The interannual differences in the thousand‐seed mass and seed setting rate increased in 2016 and decreased in 2017. Compared with the drought groups and the flood groups, the flood stress of the DFAA groups reduced the yield under drought conditions, and the decrease in the total grain number and panicles per barrel during the flood period was the main reason. The drought stress of the DFAA groups compensated the yield under flood conditions, mainly because the total grain number and seed setting rate increased during the drought period. Under long‐term light drought conditions, the panicles per barrel, grains per panicle, total grain number, thousand‐seed mass, and seed setting rate increased. Under long‐term heavy drought conditions, the panicles per barrel mainly increased.

The Huaihe River Basin is located in-between the north-south climate transitional zone in China and is China's important congested area and production base. The land-use of the environment was occupied by the land-use for social and economics. This paper aims to have a comprehensive understanding about land-use and vegetation evolution of the basin over the past 30 years. In view of 5 years (1985, 1990, 2000, 2005, and 2014) land-use data and remote sensing data about NDVI, land-use dynamic degree, and land-use transfer matrix were used to analyze the dynamics of land use. Spatial overlay was used to study vegetation change characteristics of the basin. The present study investigates the evolution trend of vegetation coverage based on spatial overlay analysis. We found that water bodies and urban lands of the basin increased during 1985-2014. On the other hand, the area of artificial vegetation, natural vegetation, and wetland were reduced. The impact was gradually increased by human intervention on various land use types. Overall vegetation coverage level shows deteriorative development, and distribution areas were discrete. The excellent vegetation coverage of natural vegetation and wetland didn't have obvious changes, but the high coverage significantly decreased. Taken as a whole, the natural vegetation coverage was reduced. The vegetation coverage level of artificial water in the land-use for social and economics had greatly increased, but the overall trend of vegetation coverage level of artificial vegetation and resident construction land showed a decreasing trend.

Changes in runoff and sediment transport in the Middle Reach of the Huai River have been studied by using 58 years of field data. The runoff yield from the Huai River watershed mainly occurs in the sub-watershed of the river. At the downstream Wujiadu station, the difference in total drainage area between the south and the north branches of the river is 43% while the difference in runoff yield is only 9%. Sediment yield mainly comes from the headwaters in the northern region with the upstream of the Huai River playing a secondary role. The data demonstrate that there has been little change in long-term average annual runoff in the Middle Reach of the Huai River (MRHR) but there has been a dramatic decrease in average annual sediment transport. This decrease in sediment transport in the Huai River has resulted in changes in the geomorphology of the Middle Reach of the Huai River with time. Further analysis indicates that both the main channel and the floodplain of the estuary of Hongzehu Lake have a tendency towards the deposition of sediment. A trend and regression analysis is used in the compilation of field data, calculations, and analysis.

Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future.

Food security is one of the main challenges facing humanity, and increasing crop yields is critical to meet the growing global food demand. Grain production in China has remarkably improved since its founding in 1949, but the growth remains uneven across regions. The goal of this research was to assess the determinants of the yields of three major food crops (maize, rice, and wheat) in China and the mechanism of their responses to increased crop yields. Boosted regression tree models were created to capture the linked complex predictor-response relationships between crop yields and individual explanatory variables using prefecture-level agricultural statistics in China coupled with geographic data during the period from 1952 to 2017. The results showed that technological inputs (e.g., fertilizers, electricity consumption, and power of agricultural machinery) played a key role in the increase of crop yields, which explained 47%, 39%, and 62% of the variances in the yields of maize, rice, and wheat during the period 1952–2017, respectively. However, the contribution of technology became weaker over time, while the contribution of agro-environmental conditions and structural characteristics of administrative regions became stronger. Partial dependence plots indicated that encouraging higher technological inputs, accelerating large-scale grain production, shortening the urban-rural income gap, and improving the education of farmers were conductive to increasing crop yields. Overall, our results suggest that grain production policies aimed at increasing crop yields should better reflect the spatial heterogeneity of yield gaps; low-yield-gap regions should improve the utilization efficiency of water and fertilizer and formulate measures to inhibit non-grain production on cultivated land, while high-yield-gap regions should focus on improving technological inputs and promoting agricultural restructuring. This study provides deep insights into the processes of how individual explanatory variables affect crop yields, which is essential to develop differentiated grain production policies and provide valuable references for shortening yield gaps in high-yield-gap regions.

Based on organizational ambidexterity theory and knowledge-based view, this study presents a conceptual framework that links organizational ambidexterity, open innovation and innovation performance together and tested this framework by distributing questionnaires to senior managers in East and Mid-China to assess related variables. The empirical study analysis based on 274 returned questionnaires finds a non-linear relationship between organizational ambidexterity and innovation performance and the moderating effects of inbound and outbound open innovation on the ambidexterity-performance relationship. To effectively use organizational ambidexterity, firms need open innovation to use external ideas as well as internal ideas, and to take advantage of both internal and external paths to market.

Biochar produced from sewage sludge could provide an important alternative to waste management practices while offering an opportunity to improve soil properties and reduce the risk of contamination from direct applications of sewage sludge soil amendments. We assessed the impacts of different rates of biochar application (20, 40, 60 t ha -1 ) to peanuts grown in a loamy sand soil in the North China Plain on composition of the soil microbial community, soil bulk density (BD), pH, total carbon (TC), total nitrogen (TN), C:N, available phosphorus (P), available potassium (K), dissolved organic carbon (DOC) and crop yield. We found that sewage sludge biochar application increased TC, TN, available K, and C:N, and decreased soil BD and pH and had variable effects on DOC. Amendment with biochar increased microbial biomass and the proportion of Gram-positive bacteria, Gram-negative bacteria, fungi and Actinomycetes, while it decreased the ratios of groups of bacteria. The highest crop yield was achieved under 40 t ha -1 of biochar. Our study suggests that the lower rates of sewage sludge biochar application could improve soil physicochemical properties and increase levels of soil microbes and crop yield; however, the highest rate may induce negative effects on microbe community composition.

In the field of hydrological modelling, the global and automatic parameter calibration has been a hot issue for many years. Among automatic parameter optimization algorithms, the shuffled complex evolution developed at the University of Arizona (SCE-UA) is the most successful method for stably and robustly locating the global “best” parameter values. Ever since the invention of the SCE-UA, the profession suddenly has a consistent way to calibrate watershed models. However, the computational efficiency of the SCE-UA significantly deteriorates when coping with big data and complex models. For the purpose of solving the efficiency problem, the recently emerging heterogeneous parallel computing (parallel computing by using the multi-core CPU and many-core GPU) was applied in the parallelization and acceleration of the SCE-UA. The original serial and proposed parallel SCE-UA were compared to test the performance based on the Griewank benchmark function. The comparison results indicated that the parallel SCE-UA converged much faster than the serial version and its optimization accuracy was the same as the serial version. It has a promising application prospect in the field of fast hydrological model parameter optimization.

The evaluations of resource and environment carrying capacity and territorial development suitability, also referred to as “double evaluations”, have been taken by China as an important direction in territorial space planning. Based on the evaluation of resource and environment carrying capacity, the double evaluations can contribute to protecting ecological safety and territorial safety and promoting regional sustainable development. The focus of this study was to integratedly evaluate the resource and environment carrying capacity of the Huaihe River Ecological and Economic Belt. First, the overall weights of the factors at the dimension level and the index level in the established integration evaluation system were calculated with the fuzzy analytical hierarchy process (FAHP) method; and then, using the linear weighted function, the overall resource and environment carrying capacities of 25 cities in the belt were calculated. On that basis, the resource and environment carrying capacity evaluation model was established. Through model analysis, this study comprehensively investigated the resource and environment carrying capacity of the Huaihe River Eco-economic Belt and provided a foundation for the future territorial space planning and layout of the Huaihe River Eco-economic Belt.

Abstract The water shortage in the Huaihe River Basin (HRB), China, has been aggravated by population growth and climate change. To identify the characteristics of streamflow change and assess the impact of climate variability and human activities on hydrological processes, approximately 50 years of natural and observed streamflow data from 20 hydrological stations were examined. The Mann–Kendall test was employed to detect trends. The results showed the following. (i) Both the natural and the observed streamflow in the HRB present downward trends, and the decreasing rate of observed streamflow is generally faster than that of the natural streamflow. (ii) For the whole period, negative trends dominate in the four seasons in the basin. The highest decreasing trends for two kinds of streamflow both occurred in spring, and the lowest ones were in autumn and winter. (iii) Based on the above analysis and quantifying assessment for streamflow decrease, human activity was the main driving factor in the Xuanwu (80.78%), Zhuangqiao (79.92%), Yongcheng (74.80%), and Mengcheng (64.73%) stations which all belong to the Huaihe River System (HRS). On the other hand, climate variability was the major driving factor in the Daguanzhuang (68.89%) and Linyi (63.38%) stations which all belong to the Yishusi River System (YSR).

It has been proved that nanosilica can improve the mechanical properties of cement soil , however, the static and dynamic properties of nanosilica modified cement soil and dispersion method study of nanosilica in cement soil were few, thereby investigated by unconfined compression test and dynamic triaxial test in this study. A series of unconfined compression test results indicated that the dispersion method of nanosilica is of importance in the strength of nanosilica modified cement soil (NCS), and soaking in water for 24 h is the optimal dispersion method among designed methods. Unconfined compressive strength (UCS) increases with increasing nanosilica content in the range of 0 to 0.4%, and then reduces gradually while nanosilica content is beyond 0.4%. The optimal nanosilica content can be seen as 0.4%, and the corresponding UCS at the curing age of 7 d is 960 kPa, which increases by 47.2% comparing to the specimen without nanosilica. Dynamic triaxial test reveals that the variation of the cumulative plastic axial strain and dynamic elastic modulus versus nanosilica content are same and opposite to UCS respectively, and the minimum of the cumulative plastic axial strain and maximum of dynamic elastic modulus are obtained while nanosilica content is 0.4%, which reduces by 48.1% and increases by 69.8% respectively comparing to the specimen without nanosilica. Finally, a simple and practical prediction model is developed to capture the evolution of the cumulative plastic axial strain with cycle number, and its simulation effect is validated by dynamic triaxial test results. It is believed that this paper finds an efficient dispersion method of nanosilica in cement soil, and provides the dynamic properties of nanosilica modified cement soil, which can promote practical application of nanosilica in road engineering. • Soaking in water for 24 h can efficiently increase the dispersion extent of nanosilica in cement soil. • 0.4% is the optimal content of nanosilica to modify cement soil, which can achieve the best strength improvement. • The proposed model has the satisfactory capability to simulate the cumulative strain behavior under cycle load.