Monitoring Points Research Articles

Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation

Foundation pit excavation will inevitably cause uneven ground settlement to pose potential risks to adjacent structures and infrastructures. To better perceive the risk status of adjacent buildings using multi-source information, a temporal-spatial-fusion-based risk assessment (TSFRA) model under the consideration of uncertainty and causality is developed by integrating FAHP (Fuzzy Analytic Hierarchy Process), DGDT (DAG-GNN, DEMATEL method, and topological analysis), and CM (cloud model). More specifically, FAHP handles temporal weights of excavation conditions based on expert knowledge. DGDT determines spatial weights for monitoring points, with DAG-GNN constructing causal graphs in a data-driven manner, DEMATEL handling global interactions, and topological analysis calculating node importance. CM can finally fuse the temporal (excavation conditions) and spatial (monitoring points) weights with settlement monitoring data, resulting in qualitative assessments of the overall risk status of adjacent buildings in uncertain environments. The proposed TSFRA is verified in the case of a Shanghai metro station construction project. Results indicate that the perceived risk of the project is at a relatively low level, consistent with the actual condition. High-risk excavation conditions and locations can be easily identified. There is a trend toward higher risk during the excavation of soft soil layers, and thus some risk control measures can be formulated to avoid potential risk events. In short, the weight determination and fusion method in TSFRA contribute to handling uncertainties in expert judgments and causalities in collected data, which can practically provide more reliable decision-making in excavation-induced risk assessment and control for ensuring the safety of adjacent buildings.

Displacement residuals reveal landslide regime shifts

AbstractDespite significant progress in the development of advanced technologies for detecting and monitoring unstable slopes, accurately predicting catastrophic landslides remains a challenge. To tackle this challenge, our research integrates advanced prediction models and granular systems theory to provide insights into regime shifts within slow-moving deep-seated landslide dynamics. Our approach is designed to discern exceptional departures from historical landslide dynamics. The approach leverages the “group dynamics,” crucial for identifying precursory failure indicators, according to the generic dynamics of the precursory failure regime in granular systems. We select three different monitored slow-moving landslides as test cases. We employ an error correction cointegration vector autoregression model together with an exogenous regressor to encode historical spatiotemporal landslide dynamics and predict displacement at multiple locations by considering the historical landslide motion and relationship with external triggers. Displacement residuals are obtained by computing the difference between predicted and measured displacement for a given historical calibration time window. Threshold values for the displacement residuals are determined by analyzing the historical distribution of these residuals. Lastly, persistence in time of the threshold exceedance and the number of monitoring points that exceed the threshold at the same time are considered to encode the group dynamics. This approach offers several advantages, including the effective identification of critical regime shifts, adaptability, and transferability, and it introduces regime shift information into local landslide early warning systems. This approach can enhance confidence in the resultant alert, particularly when integrated with conventional alert systems, thereby improving the reliability of landslide warning systems.

A multipoint prediction model for the deformation of concrete dams considering climatic features of high-altitude regions

High-altitude regions are characterized by natural climatic features such as low temperatures and high radiation. Concrete dams built in these regions face more complex operating conditions. Traditional single-output deformation prediction models ignore the correlations between different monitoring points, making it difficult to assess the overall deformation behavior of concrete dams. The black-box nature of some machine learning models can lead to a lack of interpretability, affecting the practical application. Therefore, a multipoint prediction model (MPM) is proposed to analyze the deformation behavior of concrete dams in high-altitude regions. Firstly, the deformation monitoring points are divided based on the climatic features of high-altitude regions. Subsequently, the MPM is constructed using the multi-output CatBoost model and Quasi-Monte Carlo algorithm to achieve deformation prediction across different partitioned monitoring points. Finally, the elucidation of the MPM optimization procedure in a transparent manner alongside the quantification of the influence exerted by input variables on output outcomes notably augments the interpretive capacity of the MPM. The proposed model is validated using monitoring data from a concrete dam in a high-altitude region. The disparities in chaotic and entropy variation features among deformation monitoring points in different zones corroborate the appropriateness of the zoning in this study. Comparisons between the single-output CatBoost model and MPM verify the feasibility and efficiency of multipoint predictions. Comparisons with other multi-output prediction models validate the superior predictive performance of the MPM. Additionally, the proposed model's generalization performance is validated using data from another concrete arch dam in high-altitude regions.

Effect of direct water injection on knock suppressing mechanism in a high compression ratio gasoline engine

ABSTRACT A low capacity, boosted gasoline engine is an effective approach to enhancing fuel economy. However, knock combustion poses significant limitations to the practical development of these engines. This study investigated the chemical kinetics and macroscopic properties of engines equipped with direct water injection (DWI) at high compression ratios (CRs) to address knock issues. The results indicated that the generation and consumption pathways of OH and CH2O remain unaffected by water injection. Although water exhibited an opposing effect on the sensitive reactions of OH and CH2O, the production reaction rates of OH and CH2O decrease with water injection. Knock index values at each monitoring point approached zero when the water injection quality exceeds 30%. Additionally, knock suppression effectiveness improved with the advancement of water injection timing. The simulation results indicated that the knock index at high CR decreased from 13.61 to 0.27 when the water/fuel ratio exceeded 30%. The indicated specific fuel consumption decreased by 29.3%, and indicated power increased by 41.8% compared to the original engine. Although NOx and CO emissions increased because of the high CR, the water injection coupled with the compression ratio strategy limited the increase in emissions. These findings indicated that DWI coupled with high CR was advantageous to enhancing fuel economy and reducing emissions in low capacity gasoline engines.

On flow fluctuations in ruptured and unruptured intracranial aneurysms: resolved numerical study

Flow fluctuations have emerged as a promising hemodynamic metric for understanding of hemodynamics in intracranial aneurysms. Several investigations have reported flow instabilities using numerical tools. In this study, the occurrence of flow fluctuations is investigated using either Newtonian or non-Newtonian fluid models in five patient-specific intracranial aneurysms using high-resolution lattice Boltzmann simulation methods. Flow instabilities are quantified by computing power spectral density, proper orthogonal decomposition, and fluctuating kinetic energy of velocity fluctuations. Our simulations reveal substantial flow instabilities in two of the ruptured aneurysms, where the pulsatile inflow through the neck leads to hydrodynamic instability, particularly around the rupture position, throughout the entire cardiac cycle. In other monitoring points, the flow instability is primarily observed during the deceleration phase; typically, the fluctuations begin just after peak systole, gradually decay, and the flow returns to its original, laminar pulsatile state during diastole. Additionally, we assess the rheological impact on flow dynamics. The disparity between Newtonian and non-Newtonian outcomes remains minimal in unruptured aneurysms, with less than a 5% difference in key metrics. However, in ruptured cases, adopting a non-Newtonian model yields a substantial increase in the fluctuations within the aneurysm sac, with up to a 30% higher fluctuating kinetic energy compared to the Newtonian model. The study highlights the importance of using appropriate high-resolution simulations and non-Newtonian models to capture flow fluctuation characteristics that may be critical for assessing aneurysm rupture risk.

Numerical study on heat dissipation of double layer enhanced liquid cooling plate for lithium battery module

The thermal management system's architecture is crucial for lithium batteries' efficiency and financial viability, predominantly influencing their security and longevity. We conceptualized a double-layer enhanced LCP, meticulously crafted to augment the heat dissipation capabilities of the battery assembly. This novel design targets the reduction of peak temperatures and pressure drops, fostering an even internal temperature profile. Comprehensively evaluate the performance of different settings of the coolant cooling system, and the monitoring points are strategically set in horizontal and vertical directions. Analyzing the data, it becomes evident that the system's vertical orientation significantly affects the battery module's peak temperature and temperature variation. Furthermore, extensive investigations were conducted on the impact of layout, coolant flow rate, and inlet temperature on thermal dissipation. Our findings indicate that the double-layer enhanced liquid cooling plates outperform conventional designs. There is a substantial 2.41 % decrease in the peak battery module temperature and an 80.6 % reduction in voltage decline. Operating at a flow rate of 10 g per second and an entry temperature of 25 °C, our liquid cooling system demonstrates superior cooling efficiency with minimal power consumption. It proficiently restrains the maximum battery module temperature below 30.43 °C and restricts temperature variance to merely 3.48 °C. The suggested approach in this research contributes to improved thermal equilibrium, diminishes temperature disparities and pressure concerns, thereby fostering the technological advancement of electric vehicle's liquid cooling systems.

Deep learning model for the deformation prediction of concrete dams under multistep and multifeature inputs based on an improved autoformer

The long-term prediction of deformation in concrete dams is a critical requirement for maintaining their structural integrity over time in practical management scenarios. While most existing models predominantly address short-term and medium-term predictions, there remains a significant challenge in establishing reliable correlations within complex long-term temporal patterns and varying environmental factors. Consequently, there is limited research on multistep prediction. To solve these problems, an Autoformer-based model for multistep and multifeature dam deformation prediction is proposed. In the proposed model, the sequence decomposition of dam monitoring data is used as the basic construction module inside the depth model. Then, Deep Automatic Correlation (Deep-AutoCorrelation) mechanism is employed to obtain the long-term dependence between dam deformation and environmental load. The encoder-decoder structure integrates time series decomposition and the Deep-AutoCorrelation to predict the multistep deformation of the dam. In the multistep deformation prediction of the dam, the improved Autoformer model demonstrates state-of-the-art performance, resulting in a 48% increase in prediction accuracy across five monitoring point datasets when compared to six benchmark models. Experimental results indicate that this method effectively addresses the challenge of predicting long-term deformation of dams subjected to complex environmental loads. It demonstrates robust capabilities in extracting long-term temporal features and avoids the increase of computational complexity caused by deeper time extraction.

Research and design of multi‐stage multi‐load wireless power supply system for high voltage online monitoring equipment

AbstractMany online monitoring devices in substations are placed at high potential differences from the ground, and it is not possible to supply power to these devices directly from the ground through wires. Placing magnetically coupled structures inside the substation pillar insulators for inductive power transfer is considered a feasible solution, and at this stage of research, systems that add multi‐stage repeating coil structures are mostly used to achieve stable power supply for individual online monitoring devices. However, the monitoring point generally requires multiple devices to monitor together, and the power requirements of different devices are different, so adding multiple repeating coils at the same time will increase the complexity of the system design, which is not conducive to engineering applications. Therefore, this paper designs and optimizes a multi‐stage multi‐load wireless power supply system magnetic coupling structure, using a three‐dimensional hollow solenoid combined with a ferrite core as the system repeating coil structure and an embedded magnetic coupling structure as the receiver end structure to reduce the number of system repeating coils while supplying power to devices with different power requirements. A prototype with a transmission distance of 1.02 m was built in the experiment, and the experimental results showed that the highest transmission efficiency of the system was 44.3% and 39.2% under single load and double load conditions, respectively. As the load resistance increases, the system can achieve constant voltage output under both single‐load and dual‐load operating conditions. Adjusting the number of turns of the receiving coil can change the output characteristics of the system and achieve constant voltage output of different voltage levels or constant current output of different current levels, which can meet the power supply needs of different loads.

Contamination Status and Serotypes Distribution of Salmonella in Food in Yantai City, China: A 14-Year Continuous Monitoring Study.

Salmonella is a foodborne zoonotic pathogen that threatens food safety and public health. However, few people have conducted long-term and systematic studies on Salmonella contamination in food in Yantai City. In order to investigate the situation of Salmonella contamination in food and improve the ability of early warning and control of foodborne diseases, a total of 3420 samples from 20 categories were collected from 13 monitoring points in Yantai City, from 2010 to 2023. The difference in detection rate and bacterial strain of different monitoring points, different types, and different sources of samples was compared. Of the 3420 samples, 80 were positive with a detection rate of 2.34%. Salmonella detection rates were significantly different for samples collected at different monitoring sites. Salmonella was detected only in meat and meat products and catering food, and none of the other types were detected. The detection rate of Salmonella was higher in raw animal meat and raw poultry. Samples collected at the market stage had the highest detection rate (5.81%), and there was a significant difference in detection rate between samples from different sources (χ2 = 36.93, p < 0.05). Eighty-one strains of Salmonella were detected out of 3420 samples (2 different strains were detected in 1 positive sample). The serological test identified 8 groups and 27 serotypes. The dominant serum groups were group B 30.86% (25/81), group E1 23.46% (19/81), and group D 16.05% (13/81). The main dominant serotypes were Salmonella give 17.28% (14/81), Salmonella enteritidis 16.05% (13/81), and Salmonella derby 13.58% (11/81). Meat and meat products and catering food were the main food products contaminated with Salmonella. The resulting secondary contamination is the hidden threat of foodborne diseases and should be given sufficient attention.

Study on Thermal Runaway Behavior and Jet Characteristics of a 156 Ah Prismatic Ternary Lithium Battery

Ternary lithium batteries have been widely used in transportation and energy storage due to their high energy density and long cycle life. However, safety issues arising from thermal runaway (TR) need urgent resolution. Current research on thermal runaway in large-capacity ternary lithium batteries is limited, making the study of hazard indicators during the thermal runaway ejection process crucial. This study places a commercial 156 Ah prismatic battery (positive electrode material: Li(Ni0.8Mn0.1Co0.1)O2, negative electrode material: graphite) in a nitrogen-filled sealed container, triggering thermal runaway through lateral heating. The experimental results show that the battery’s maximum surface temperature can reach 851.8–943.7 °C, exceeding the melting point of aluminum. Temperature surge inflection points at the battery’s bottom and near the small side of the negative electrode coincide with the inflection point on the heated surface. The highest jet temperatures at three monitoring points 50 mm, 150 mm, and 250 mm above the safety valve are 356.9 °C, 302.7 °C, and 216.5 °C, respectively. Acoustic signals reveal two ejection events. The average gas production of the battery is 0.089 mol/Ah, and the jet undergoes three stages: ultra-fast ejection (2 s), rapid ejection (32 s), and slow ejection (47 s). Post-thermal runaway remnants indicate that grooves from internal jet impacts are mainly located at ±45° positions. This study provides valuable insights for the safety design of batteries and the suppression of thermal runaway propagation.

Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry

Abstract Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.&amp;#xD;

Study on transient cavitation performance of centrifugal pump based on the influence of rough impeller

This study employs numerical simulation to investigate the transient flow and cavitation performance of centrifugal pumps with rough impellers, validating the numerical method with experimental data. Initially, the effect of blade roughness on the external characteristics of centrifugal pumps is examined. Subsequently, the study specifically addresses the impact of roughness on internal flow characteristics during cavitation, including vapor volume distribution, three-dimensional vortex structures, and vorticity distribution in the impeller channel. Furthermore, the influence of blade roughness on local energy loss is analyzed using entropy production theory. Finally, several monitoring points are arranged in the impeller channel to assess pressure pulsation effects. The results show that blade roughness generally reduces the head and efficiency of centrifugal pumps. During the non-cavitation and cavitation incipient stages, roughness marginally increases the head, with a maximum increase in only 0.1%. Impeller roughness causes vacuole collapse and vortex structure enlargement, disrupting the stable flow path within the channel. Blade roughness also escalates energy loss within impeller components, particularly under full cavitation conditions, where the impeller's entropy production accounts for up to 50%. Pressure pulsation results reveal that while blade roughness can slightly suppress cavitation, it also disturbs the flow field pressure. These insights provide guidance and data support for mitigating roughness and cavitation, the two primary instability factors in centrifugal pump operations.

Early-onset scoliosis in children aged 4-7 years in Nanjing, China: A cross-sectional study.

This study aimed to investigate the potential variance in the prevalence of early-onset scoliosis among children aged 4-7 years and analyze the influencing factors. The goal was to establish a crucial reference point for monitoring and evaluating spinal curvature development in preschoolers, ultimately to reduce the occurrence of adverse health outcomes. Children aged 4-7 years within the main urban area of Nanjing were selected using a stratified random sampling method. A team of four senior therapists conducted screenings for spinal curvature among children using visual inspection, the Adams forward bending test, and an electronic scoliometer to measure the angle of trunk rotation (ATR) and identify children displaying signs of scoliosis. Children with suspected scoliosis in the initial screening underwent X-ray Cobb angle assessment for confirmation. The prevalence of early-onset scoliosis was then determined from the screening results. R version 4.2.0 software was used to analyze the factors associated with scoliosis among children using partial least squares structural equation modeling. A total of 2281 children were included in this study, consisting of 1211 boys and 1070 girls, with a mean age of 5.44 ± 0.81 years (ranging from 4 to 7 years). Among them, 7.58% exhibited positive signs of scoliosis, 5.87% had early-onset scoliosis, and the positive predictive value was 77.5%. Significant differences in ATR were observed among children in different age groups (Kruskal-Wallis = 15, p = 0.0104) and by sex (t = 3.17, p = 0.00153). Significant variations in ATR were noted in children with scoliosis (t = -22.7, p < 0.001), with a cutoff at ATR = 4.5°, and auxiliary values of 0.947 and 0.990. Children diagnosed with early-onset scoliosis generally exhibited lower body mass index values, with a statistically significant difference (t = 2.99, p = 0.003). Using visual inspection, the Adams test, and an electronic scoliometer to measure the ATR, the present triad method is more sensitive for early scoliosis screening in children with abnormal posture aged 4-7 years. A full spine X-ray is advised in children with an ATR over 4.5° and poor posture.

Use of lichens as bioindicators of contamination by agrochemicals and metals.

The presence or absence of lichens serves as an indicator of the condition of an ecosystem and the degree to which it is contaminated by various agents, such as agrochemicals and metals. Evaluating the use of lichens as bioindicators of agrochemical contamination could provide a more comprehensive perspective of current contamination levels. Monitoring was conducted over a 4-month period in two study areas: one was a well-conserved area contaminated by metals, and the other was an area surrounded by agricultural crops contaminated by agrochemicals. Data on the presence and abundance of lichens in each study area were recorded at 10 monitoring points, a procedure that was repeated 16 times (every 15days), and concentrations of heavy metals and "organophosphate" agrochemicals in the lichens collected were measured by means of Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-OES) and Gas Chromatography (GC), respectively. Generalized linear mixed models were used to assess abundance and richness, while general linear mixed models were used to attain Shannon diversity and Simpson dominance indices. Moreover, a multivariate analysis was performed in order to compare the lichen communities in both areas. The results indicated differences between the area contaminated by metals and that contaminated by agrochemicals in terms of abundance and Simpson's dominance index, while no differences were found in the case of the richness and diversity models. The PERMANOVA analysis additionally showed differences between the lichen communities in the two areas. The results also demonstrated that Canoparmelia caroliniana bioaccumulated metals in both areas. The levels of barium, cadmium, and sodium were higher in the area contaminated by metals, while concentrations of chromium and copper were higher in the area contaminated by agrochemicals. Finally, the concentrations of agrochemicals were higher in the area contaminated by agrochemicals and included toxic substances such as Methylparathion and Parathion, which are prohibited in Ecuador. In conclusion, this research underscores the importance of lichens as precise indicators of environmental health and contamination by agrochemicals and metals.

Assessment of the Impact of Coal Mining on Water Resources in Middelburg, Mpumalanga Province, South Africa: Using Different Water Quality Indices

The objective of this study was to assess the water quality status of the surface water and groundwater resources in the Middelburg area, South Africa. The assessment was addressed using combined water quality indices, investigating selected chemical parameters over four different seasons for a period of five years from 2017 to 2021. A combination of the Canadian Council of Ministers of the Environment water quality index and the comprehensive pollution index was used to analyze the water quality status of surface water and groundwater of the town of Middelburg, situated near coal mining activities in Mpumalanga, South Africa. The combination of the indices indicated that some surface water monitoring sites ranged between poor to fair water quality. Groundwater monitoring points also showed a poor to fair ranking. The comprehensive pollution index confirmed that some sites showed very poor water quality in the summer seasons, exceeding expected limits for the period 2017 to 2021. The principal component analysis further showed that both surface water and groundwater sites had high levels of contamination with increased chemical parameters. The results were compared against the different water quality guidelines. In an extensive monitoring program, water management systems must be properly implemented to mitigate impacts on water resources.

Microbial Metagenomics Revealed the Diversity and Distribution Characteristics of Groundwater Microorganisms in the Middle and Lower Reaches of the Yangtze River Basin.

Groundwater is one of the important freshwater resources on Earth and is closely related to human activities. As a good biological vector, a more diverse repertory of antibiotic resistance genes in the water environment would have a profound impact on human medical health. Therefore, this study conducted a metagenomic sequencing analysis of water samples from groundwater monitoring points in the middle and lower reaches of the Yangtze River to characterize microbial community composition and antibiotic resistance in the groundwater environment. Our results show that different microbial communities and community composition were the driving factors in the groundwater environment, and a diversity of antibiotic resistance genes in the groundwater environment was detected. The main source of antibiotic resistance gene host was determined by correlation tests and analyses. In this study, metagenomics was used for the first time to comprehensively analyze microbial communities in groundwater systems in the middle and lower reaches of the Yangtze River basin. The data obtained from this study serve as an invaluable resource and represent the basic metagenomic characteristics of groundwater microbial communities in the middle and lower reaches of the Yangtze River basin. These findings will be useful tools and provide a basis for future research on water microbial community and quality, greatly expanding the depth and breadth of our understanding of groundwater.

Analysis of the Quality of the River Kualuh in the Review of the Physical-Chemical

The purpose of this study is to analyze the water quality of the river and the status of water quality of watersheds Kualuh Kabupaten Labuhanbatu. Sampling was done at 3 monitoring points along the flow of the river Kualuh Kabupaten Labuhanbatu. The determination of the sampling points in the field using the purposive sampling method, with the determination of the sampling points based on the differences in the characteristics found in the study area.Some of the parameters measured were temperature, conductivity, COD, BOD, and oil/ fat are then compared with the criteria of the Raw water Quality of the river class II according to PP R. I. No. 82 Year 2001.On Water Quality Management.Based on the results of the analysis of the obtained results that in the area of monitoring point 1, is the most polluted areas.This is because, in this area there is the activity of a diverse community.Household produces waste that comes from the activity of the bathroom, toilet, kitchen, washing clothes and washing of household appliances.

Research and application of surface spray anti-corrosion technology for coiled tubing in pressurized environment

In this study, the corrosion risk of coiled tubing under high pressure, high temperature and high sulfur conditions was investigated, and the protection solution was put forward. In this paper, spray paint is used to prevent corrosion of tubing, and the effects of high temperature desulfurization and nano super absorbent spraying agent are compared. The pressure jet spraying device is designed for uniform spraying during operation to improve corrosion resistance. Determine the best spraying position and monitoring point. The results show that nano-coating agent can prevent cracking and serious corrosion, and its application effect in oil wells meets the requirements. These findings support that anti-corrosion spraying technology on coiled tubing surface can effectively prevent tubing corrosion in high temperature, high pressure and corrosive environment.

Study on microstructure of cemented paste backfill with initial defects under uniaxial compression failure

In order to fully understand the micro and fine structural characteristics of the cemented paste backfill (CPB) with initial defects, this paper carries out uniaxial compression test on the CPB and real-time monitoring with DIC technology, analyses the crack extension and deformation characteristics on the surface of the specimen, carries out SEM test on the damaged specimen, binarizes the SEM images, and performs microscopic pore analysis on the processed binarized images using PCAS software. Then some parameters such as porosity, probability entropy, probability distribution index, fractal dimension, form factor, etc. of the specimens with different initial defects are discussed. The study shows that the damage form of CPB under uniaxial compression is mainly tensile damage, and gradually changes from tensile damage to shear damage when the specimen reaches the peak strength. The overall displacement and strain of each monitoring point on the specimen increases rapidly after entering the plastic deformation stage. There are many small microcracks and pores in the CPB, and there are a large number of aciculate ettringite and C-S-H gel hydration products, which are generated in the sample unevenly. With the addition of air-entraining agent (AEA), within a certain range, the number of pores inside the CPB increases. Many small pores also form large pores because of the connectivity between the pores, the porosity of the specimen increases, and the morphology and structure of the pores are relatively more regular. However, the direction of the pore development still does not have good directionality, and the initial defects become increasingly obvious.

Predicting the Deformation of a Slope Using a Random Coefficient Panel Data Model

Engineering constructions in coastal areas not only affect existing landslides, but also induce new landslides. Variation of the water level makes the coastal area a geological hazard-prone. Prediction of the slope displacement based on monitoring data plays an important role in early warning of potential landslide and slope failure, and supports the risk management of hazards. Given the complex characteristic of the slope deformation, we proposed a prediction model using random coefficient model under the frame of panel data analysis, so as to take the correlation among monitoring points into consideration. In addition, we classified the monitoring data using Gaussian mixture model, to take the temporal-spatial characteristics into consideration. Monitoring data of Guobu slope was used to validate the model. Results indicated that the proposed model have a better performance in prediction accuracy. We also compared the proposed model with the BP neural network model and temporal – temperature model, and found that the prediction accuracy of the proposed model is better than those of the two control models.