The maturity method is deservedly considered one of the reliable indirect methods for determining the strength at the early stages of concrete curing. The main parameter in the calculation is the internal temperature of concrete that accumulates during the chemical reaction of concrete curing, while external factors such as ambient temperature and relative humidity are fallaciously omitted. In this work, the complex maturity method was developed based on ASTM C1074, accounting for the influence of ambient temperature and relative humidity and coefficients indicating their influence weight. The laboratory testing to measure the concrete strength by compression method and non-destructive sclerometer method were performed on concrete samples according to ASTM C109, GOST 22690, and GOST 10180. According to the calibration dependence of the existing and proposed methods results in comparison with the strength of cubic samples, the highest coefficient of determination R² = 0.976 was revealed for the complex method of maturity, which indicates its reliability in contrast with sclerometer and traditional maturity methods. The determination of complex maturity allows for an evaluation of the current state of concrete strength, but also reduces the waiting time for concrete curing and increases the economic effect during construction.

A fast reduction of a reservoir level may result in instability of an earth dam caused by the high pore water pressures that remain relatively high in the embankment. Moreover, the dissipation of the accumulated pore water pressures is highly dependent on the permeability of the materials used for the embankment and the storage characteristics of the reservoir. Therefore, in the design of embankment dams, the stability analysis under rapid drawdown loading conditions is an important design case. In this study, the influence of different permeability rates on dam stability under different cases of rapid drawdownwas investigated using the finite element method in SEEP/W and SLOPE/W of the GeoStudio with a case of the Lugoda dam in Ndembera catchment, Tanzania. The modeling process considers the time-dependent hydraulic conditions and the transient flow conditions using different water levels during rapid drawdown for evaluation of the factor of safety. From the 1m per day drawdown rate; the lowest minimum factor of safety value (0.90) was obtained from the 10 -7 m/s material permeability of the upstream zone of the dam. It means that, at a drawdown rate of 1m per day, there is a potential for failure of the embankment if the hydraulic conductivity value will be somewhere below 10 -6 m/s.

This study investigated the potential influence of operating water levels and loading conditions on the slope stability of an embankment dam. Four different operating reservoir levels (normal, reduced, embankment height, and overflow) were considered in the study. Numerical modeling was used to investigate the problem in the case of the Chardara dam within the Syrdarya catchment in Kazakhstan. Based on the drawdown rates and operating conditions, minimum factor of safety values ranging from 0.56 (total failure) to 2.5 were retrieved. Furthermore, a very high correlation was observed between drawdown days, the minimum factor of safety values, the maximum factor of safety values, and pore-water pressures, with correlation coefficients ranging from 0.561 to 0.997 (strong to very strong correlation). On the other hand, the highest negative correlation of 0.997 was observed between the minimum factor of safety values and pore-water pressures. Additionally, based on the results from the analysis of variance, three reservoir operating levels (normal, embankment height, and overflow) resulted in p-values less than 0.05, indicating that the variations in the factor of safety values from the drawdown rates were statistically significant. The findings of this study demonstrated that, not only may the drawdown rate be detrimental to the embankments, but that different operating levels can also affect slope stability in different ways.

Concrete strength gain can be significantly affected by the initial characteristics of the raw materials. Unfortunately, information on the potential influence of the initial water pH on concrete strength gain is still scarce. In this study, the potential effects of the initial water pH on concrete strength gain were investigated using a combination of sensors and a sclerometric test. The impact of initial pH on the strength gain process was investigated using three distinct pH values (4.0, 7.0, and 12). The primary variables examined were pH variations over time, internal temperature, and strength gain. The problem was further examined using a number of statistical techniques, including Single-way Analysis of Variance, Scheffé’s approach, and Correlation Matrixes. When the temperature data from 4.0, 7.0, and 12 pH values were put through the Analysis of Variance, a p-value of 2.4 × 10⁻²⁶¹ was retrieved. Additionally, when the strength gain data from 4.0, 7.0, and 12 pH values were subjected to the Analysis of Variance, a p-value of 2.9 × 10⁻¹⁶⁸ was retrieved. The results showed that the differences in the list data retrieved from the investigated pH values were statistically significant. Based on the results, we can state that the initial pH level in the mixing water can have noticeably varied consequences in terms of the strength gain of the concrete and should be carefully considered during the preparation process of concrete. The findings retrieved from this study provide a piece of useful information in the construction field, especially with concrete strength management.

The study examined the intricate relationships between embankment slope configurations, toe drain designs, and drawdown scenarios. It utilized a unique combination of numerical, physical, and mathematical models. The investigation involved 16 numerical models and 8 physical models with distinct characteristics. The research explored the correlations of key parameters: matric suction, horizontal water conductivity, time, and factor of safety. The factor of safety values varied from 0.62 to 1.03 as a result of the different investigated combinations. For instance, a 1:2 embankment slope without a toe drain under instantaneous drawdown led to the factor of safety values ranging from 1.22 to 1.57. Additionally, incorporating elements like a 30 m toe drain and a 1 m per day drawdown rate influenced these values, with extremes recorded from 1.337 to 2.21, shedding light on embankment stability under diverse conditions and configurations. When subjected to a 1 m per day drawdown, water flow rates decreased significantly at the upstream face and increased downstream, accompanied by an increase in water mass flux at the upstream face and a decrease at the downstream toe, suggesting dynamic changes in water behavior in response to drawdown. Moreover, the findings unveiled significant correlations between matric suction and time (correlation coefficient of 0.950) and factor of safety and water conductivity (correlation coefficient of 0.750). Conversely, a distinct negative correlation emerged between matric suction and factor of safety (correlation coefficient of −0.864). The study’s distinctive insights contribute to our understanding of seepage behavior and dam stability across varied scenarios, offering valuable input for resilient dam construction approaches that will ensure the longevity and effectiveness of these essential structures.

To support bearing capacity estimates, this study develops and tests a geoprocessing workflow for predicting soil properties using Empirical Bayesian Kriging 3D and a classification function. The model covers a 183 m × 185 m × 24 m site in Astana (Kazakhstan), based on 16 boreholes (15–24 m deep) and 77 samples. Eight geotechnical properties were mapped in 3D voxel models (812,520 voxels at 1 m × 1 m × 1 m resolution): cohesion (c), friction angle (φ), deformation modulus (E), plasticity index (PI), liquidity index (LI), porosity (e), particle size (PS), and particle size distribution (PSD). Stratification patterns were revealed with ~35% variability. Maximum φ (34.9°), E (36.6 MPa), and PS (1.29 mm) occurred at 8–16 m; c (33.1 kPa) and PSD peaked below 16 m, while PI and e were elevated in the upper and lower strata. Strong correlations emerged in pairs φ-E-PS (0.91) and PI-e (0.95). Classification identified 10 soil types, including one absent in borehole data, indicating the workflow’s capacity to detect hidden lithologies. Predicted fractions of loams (51.99%), sandy loams (22.24%), and sands (25.77%) matched borehole data (52%, 26%, 22%). Adjacency analysis of 2,394,873 voxel pairs showed homogeneous zones in gravel–sandy soils (28%) and stiff loams (21.75%). The workflow accounts for lateral and vertical heterogeneity, reduces subjectivity, and is recommended for digital subsurface 3D mapping and construction design optimization.