Behavior Of Dams Research Articles

Swimming exercise strain-dependently affects maternal care and depression-related behaviors through gestational corticosterone and brain serotonin in postpartum dams

Pregnancy is a very complex and highly stressful time in which women become more physically and emotionally vulnerable. Therefore, mothers are more likely to face decreased self-esteem and increased postpartum depression. Despite the high prevalence of postpartum depression, more than 50 % of mothers are undiagnosed or untreated, showing an urgent need to explore an effective preventive strategy. A healthy lifestyle and regular physical activity have been suggested to be associated with an increased quality of life in pregnant and postpartum women. The purpose of this study was to determine whether swimming exercise before and during pregnancy can affect maternal care and postpartum depression-related behaviors in dams. To this end, female NMRI and C57BL/6 J mice were subjected to swimming exercise before conception and throughout pregnancy. On postpartum days 1–2, maternal behavior including nest building, active nursing, and licking/grooming were monitored. A battery of behavioral tests was also used to measure depression-related symptoms including anhedonia- and anxiety-like behavior, social behavior, and behavioral despair. To identify the underlying mechanisms, corticosterone and inflammatory cytokines during late pregnancy, and corticosterone and brain serotonin during the postpartum period were measured in dams. The findings indicated that swimming exercise increased gestational corticosterone, decreased maternal care and brain serotonin, and increased all depression-related behaviors in postpartum C57BL/6 J dams, while only increased licking/grooming and social behavior, and reduced anhedonia-like behavior in postpartum NMRI dams. Taken together, this study suggests that swimming exercise before and during pregnancy could alter maternal care and postpartum depression-like behavior in a strain-dependent manner.

An advanced Bayesian parameter estimation methodology for concrete dams combining an improved extraction technique of hydrostatic component and hybrid response surface method

It is very important to estimate the mechanical parameters based on monitoring data for investigating the operation behavior of concrete dams. In this paper, an advanced Bayesian parameter estimation method for concrete dams is proposed. The Empirical Mode Decomposition (EMD) is adopted to extract the aging component of the observed deformation sequence, and the hydrostatic component is separated from the remaining periodic term through the statistical regression model (SRM). A hybrid response surface method (HRSM) is proposed as a surrogate model for finite element method (FEM) simulation. The likelihood function, which reflects the error between the observed and simulated deformation, is constructed, then the Bayesian formula for parameter estimation is established. The transitional Markov chain Monte Carlo (TMCMC) algorithm is used to obtain the parameter posterior distribution. The proposed method is applied to estimate the elastic modulus of the GD concrete dam, the parameter posterior distribution is obtained through the fusion of monitoring information and parameter prior information. This method can also avoid the issues of multiple solutions and unreasonable parameter combinations, which may exist in traditional deterministic inversion methods. The case study shows that the proposed method has good applicability in the parameter estimation of concrete dams.

Effect of Organic Amendment on the Physicochemical Characteristics of Tailings Dam Soil and Root Development of Tree Species, Fifteen Years After Planting

Among mine wastes, tailings are known to have the largest environmental impact, as they have high concentrations of trace elements and are susceptible to wind dispersal and water erosion. A tree plantation trial was installed at Kipushi tailing (DR Congo) in order to mitigate the contaminant dispersal in the surrounding areas. Fifteen years later, the present study was conducted for the purpose of investigating the macronutrient and metal content in amended holes and assessing the performance of tree species through root behavior in the tailings dams. Results show elevated available P, K, Ca, and Mg concentration in the surface and amended layers, which is higher than the unpolluted soil of the miombo woodland. Trace metals were manifold higher compared to the pedo-geochemical background of the region, with Cu and Co concentration tending to increase in the organic matter-rich layers, while Zn, Cd, Pb, and As remained higher in tailings. Compared to the tailing layer, roots grew well in the amended layers, but few roots ranging from very fine to big from all the surviving species were able to grow beyond the amended layers, indicating the possibility of tree survival on the tailings dams over many years. Acacia polyacantha and Psidium guajava are species that showed a higher quantity of roots in the unamended tailing layers. Leaves should be avoided for human or animal consumption, but as the concentration of Cu, Zn, and Co in guava was lower, there is no indication of hazards in case of their consumption. Therefore, the use of well-adapted tree species on the mix-up of the organic amendments with the uncontaminated topsoil seemed to be a good technique for the reclamation of larger polluted areas.

Long-term dam behavior prediction with deep learning on graphs

Abstract Dam displacement prediction is one of the most crucial considerations for ensuring the dam’s long-term safe operation. Most existing models focus on predicting individual displacement and ignore the spatial and temporal correlation of data. To address these issues, a novel prediction model based on attention mechanism and graph convolutional network is proposed. To extract the spatial and temporal correlation of the original data, the position embedding and aggregation modules are employed in the prediction model. Through the aggregation module, a spatial-temporal graph is constructed. The spatial-temporal chart connects spatial diagrams of different time steps together. To capture the spatial-temporal features in the constructed graph, a recurrent graph convolutional module is employed. Through the recurrent graph convolutional module, the spatial-temporal features are used to predict the dam displacement. For verification, an arch dam is taken as an example. Comparing eight baseline models, the proposed model is more effective than other prediction models. Therefore, the proposed model can be adapted for engineering applications.

Distinct behavior in early life stress dams predicts heterogeneity in future stress response in offspring over lifespan

IntroductionExposure to early life stress (ELS) strongly predicts prevalent, impairing, and costly psychiatric illness throughout life including mental disorders. The reason, some individuals are more vulnerable to ELS whereas others remain resilient, is poorly understood. There is a need for better understanding of early biological changes triggered by ELS with responsibility to negative outcomes in health.ObjectivesWe stratify animals after ELS according to corticosterone levels. [1] Re-challenging the animals to a second stressor, chronic social defeat (CSD) [2], in adulthood was performed to understand individual trajectories depending on corticosterone exposure during early adverse conditions.MethodsWe performed ELS as previously reported [1]. Behavior of mothers was observed during ELS. Correlation between level of corticosterone and behavior observed in dams. ELS animals were exposed to a second stress in adulthood. A battery of tests for different behavioral domains was performed. Behavioral analyses was combined with assessment of litter HPA system reactivity and observed behavior in dams.ResultsStress dams where significantly higher in number of sorties over whole observation period, time dams spent outside the nest differed. We could correlate the number of sorties on p3 with corticosterone plasma level at p9. Control dams spent significantly more time outside in 9pm recordings than stress animals. We could show reduced interaction with social juvenile targets in sociability test for CSD mice. Light dark transition was significantly higher for control mice compared to CSD but lower for control vs ELS animals.ConclusionsBehavior in dams during ELS correlates with chronic stress coping mechanisms in offspring’s adulthood.DisclosureNo significant relationships.

Multi-sensor real-time monitoring of dam behavior using self-adaptive online sequential learning

The behavior monitoring model is the most widely used method in dam health monitoring, but existing methods still concentrate mainly on offline modeling or batch learning, neglecting the timeliness requirement. This paper describes an online model based on sequential learning for real-time monitoring of dam displacement behavior. The proposed method involves two major modeling stages. First, kernel principal component analysis (KPCA) is used for multi-sensor data fusion to extract the more essential contextual components and remove the multicollinearity from the raw sensory data. Second, a novel self-adaptive online sequential extreme learning machine (SOS-ELM) is presented to efficiently capture the complex nonlinear mapping from environmental variables to displacements by coupling the classic OS-ELM with regularization technique and forgetting mechanism. The proposed model is verified using monitoring data of a real-world concrete dam. The results show that the proposed sequential model can obtain satisfactory prediction accuracy with a low computational cost, and hence, is a competitive modeling tool for dam behavior monitoring.

FEM-Bayesian Kriging method for deformation field estimation of earth dams with limited monitoring data

The health monitoring and operation behavior assessment of earth dams are research focuses in geotechnical engineering. Due to the limited monitoring data and the uncertainty of material properties, existing monitoring data-based interpolation methods and mechanism-based finite element method (FEM) can not accurately estimate the dam deformation field. Therefore, studying an accurate estimation method of the deformation field is important to guarantee the serviceability of earth dams. In this paper, a FEM-Bayesian kriging (FBK) method is proposed, in which the FEM is introduced into the Bayesian kriging framework as the source of soft data, taking the FEM results as a trend surface of the deformation field, the spatial distribution of the random residual is determined by the exact monitoring data, thereby the FEM results are corrected. In this method, data-rich but low-precision FEM results and limited but high-precision monitoring data are coupled to approach the real deformation field. A numerical experiment is designed to verify the accuracy of the proposed method, and the estimation error of the FBK is reduced by about 80% compared with traditional methods. The case study of an actual earth dam also shows that the proposed method can effectively characterize the deformation behavior of earth dams.

Effects of bedrocks on the dynamic response of concrete gravity dams subjected to underwater explosions

Reservoirs and dams often play critical roles in economics and politics. They are frequently considered as the primary targets during wartime or in terrorism attacks. In this study, the behaviors of concrete gravity dams subjected to underwater explosions are numerically investigated. Three-dimensional finite element models are developed according to the design drawings of a concrete gravity dam built in China. The effects of an underwater explosion, including the shock wave and bubble oscillations, are modeled based on the pressure time-histories. The water is modeled using the acoustic method, for which the propagation of the pressure histogram is well reproduced as an acoustic wave. The concrete dam is modeled using solid elements. The damage and failure of the concrete are reproduced by the damage-plasticity model with strain rate dependence. To investigate the influence of bedrocks, three types of numerical models are developed, which are validated by comparing the predictions with the centrifuge tests on a scale concrete gravity dam. Systematic numerical results indicate that the developed numerical model is capable of capturing the features of concrete gravity dams subjected to underwater explosions. Furthermore, the bedrocks play an important role in the dynamic responses of the dams. Thus, the bedrocks should be considered for the accurate modeling of the underwater explosion systems of concrete gravity dams.

Modelling of in Earthen Dam Under the Effect of Seismic Loading, Case of the Taksebt Dam (Algeria)

Abstract The prediction of displacements in earthen dams after seismic loading is necessary to ensure their proper functioning. In this study, the finite element software plaxis 2D is used to model the nonlinear dynamic behavior (elasto-plastic) of the embankment dam solicited by real seismic records. The earth dam considered in our case study is the Taksebt dam located in the north-east of Algeria. The main steps of the modeling are the following: first the dam of Taksebt was analyzed under seismic stresses without water (empty). Then the dam of Taksebt is subjected to the same seismic records with water (full). The comparison of the study cases allows us to estimate the displacements in the two main directions (horizontal and vertical).The results obtained show the ability to estimate the displacements in an embankment dam under seismic excitation. In addition, the analysis time will be reduced considerably by considering the two extreme cases of seismic loading.

Parental exposure of Tadalafil has beneficial effect on reflexive motor behaviors in mice offspring.

Tadalafil has positive effects on neurodevelopment and antioxidant defense system, but there is no information for its possible role during gestation on reflexive motor behavior in offspring. So current study determined the effect of prenatal exposure to the Tadalafil on reflexive motor behaviors and antioxidant activity in mice offspring and antidepressive behaviors in postpartum dams. Forty pregnant female NMRI mice were allocated into four groups. In control group, mice received water while in Groups 2-4, female mice orally gavage with Tadalafil (0.4, 0.8, and 1.6mg/kg) at gestation day (GD) 5, 8, 11, 14, and 17, respectively. Following delivery, pups were selected and reflexive motor behaviors determined using ambulation, hind-limb foot angle, surface righting, hind-limb strength, grip strength, front-limb suspension, and negative geotaxis tests. Also, serum malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GPx), and total antioxidant status (TAS) were determined in offspring. On Day 2 postpartum, antidepressant activity of Tadalafil was determined by open field test (OFT), rotarod, forced swimming test (FST), and tail suspension test (TST) in dams. Based on the findings, maternal exposure to Tadalafil improved ambulation score, hind-limb suspension score, grip strength, and front-limb suspension in offspring (P < 0.05). Prenatal exposure to Tadalafil decreased surface righting, hind-limb foot angle, and negative geotaxis in offspring (P < 0.05). Tadalafil decreased blood MDA and increased SOD and GPx levels in offspring (P < 0.05). Tadalafil significantly decreased immobility time in FST and TST and increased number of squares crossed in OFT and spending time on rotarod on postpartum mice (P < 0.05). These results suggested that parental exposure of Tadalafil has positive effect on reflexive motor and postpartum behaviors.

Coupling machine learning and stochastic finite element to evaluate heterogeneous concrete infrastructure

Probabilistic analyses facilitate the incorporation of inherent uncertainties in dam safety evaluation. Their application is limited due to the high computational burden, especially for the complex transient analyses and the cases in which the random field theory is applied to account for spatial uncertainty in material properties. In this work, uncertainty in the heterogeneous concrete is modeled by a series of probabilistic seismic finite element simulations. The outcome of stochastic models is fed into a random forests model for further post-processing and sensitivity analysis. The challenging aspect of this research lies in the fact that the initial number of finite element realizations is relatively small (due to computational time) regarding the random variables. Despite such an unfavorable setting, the meta-model showed reasonable accuracy for predicting the maximum displacement at the crest and the maximum stress at the base. A comprehensive discussion is provided on the variable importance and the partial effect of the inputs: the areas in the dam body with the highest impact in the response are identified, and the spatial distribution of input material properties is appropriately captured. These results show the possibilities of random forests in high-dimensional problems for predicting and interpreting dam behavior.

Prediction of Concrete Dam Deformation through the Combination of Machine Learning Models

Dam safety monitoring is of vital importance, due to the high number of fatalities and large economic damage that a failure might imply. This, along with the evolution of artificial intelligence, has led to machine learning techniques being increasingly applied in this field. Many researchers have successfully trained models to predict dam behavior, but errors vary depending on the method used, meaning that the optimal model is not always the same over time. The main goal of this paper is to improve model precision by combining different models. Our research focuses on the comparison of two successful integration strategies in other areas: Stacking and Blending. The methodology was applied to the prediction of radial movements of an arch-gravity dam and was divided into two parts. First, we compared the usual method of estimating model errors and their hyperparameters, i.e., Random Cross Validation and Blocked Cross Validation. This aspect is relevant not only for the importance of robust estimates, but also because it is the source of the data sets used to train meta-learners. The second and main research topic of this paper was the comparison of combination strategies, for which two different types of tests were performed. The results obtained suggest that Blocked CV outperforms the random approach in robustness and that Stacking provides better predictions than Blending. The generalized linear meta-learners trained by the Stacking strategy achieved higher accuracy than the individual models in most cases.

Developmental administration of valproic acid alters DNA methylation and maternal behavior.

Exposure to adversity in early development has powerful and potentially lasting consequences on behavior. Previous work in our laboratory using female Long-Evans rats has demonstrated that exposure to early-life maltreatment manifests into alterations in dam behavior, including a perpetuation of the maltreatment phenotype. These observed behavioral changes coincide with changes in epigenetic activity in the prefrontal cortex (PFC). Further, treating dams with a chromatin modifying agent (Zebularine) normalizes methylation and maltreatment phenotypes, suggesting a link between epigenetic programming and phenotypic outcomes. Here, we sought to investigate if administration of a chromatin modifying agent concurrent with the experience of maltreatment normalizes epigenetic activity associated with maltreatment and alters behavioral trajectories. Administration of valproic acid (VPA) transiently lowered levels of global DNA methylation in the PFC, regardless of exposure to nurturing care or maltreatment. When VPA-exposed animals reached adulthood, they engaged in more adverse behaviors toward their offspring. These data provide further evidence linking epigenetic changes in the developing brain with effects on behavior.

The Impacts of Early-life Adversity on Striatal and Hippocampal Memory Functions

The impacts of early-life adversity (ELA) on cognitive functions including striatal-dependent habit memory and hippocampal-dependent spatial memory were investigated in male mice. The ELA mouse model was generated via an altered cage environment with limited nesting and bedding materials during postnatal days 2–9 (P2–9). The altered cage environment affected the nesting behaviors of dams, creating a stressful condition for their offspring. The ELA mice had biased decision making and poor spatial memory when they grew into young adults (4-month-old). To explore the underlying synaptic basis of these effects, excitatory synapses represented by postsynaptic density protein-95 (PSD-95) were immunolabelled on a series of brain sections and stereologically quantified in the dorsomedial striatum (DMS) and dorsolateral striatum (DLS), as well as in area CA1 of the dorsal hippocampus. Increased PSD-95-immunoreactive synapses were observed in DLS but not DMS, whereas selective loss of PSD-95 synapses was detected in the stratum radiatum of area CA1. The spine data supported the selective effects of ELA on PSD-95 synapses. Specifically, both thin and mushroom-type spines were increased in DLS, while loss of thin spines was apparent in CA1 radiatum in ELA mice versus controls. The correlation between PSD-95 synapses and memory performances was further analyzed, and the data suggested that increased small (<0.20 μm3) and large (>0.40 μm3) synapses in DLS might drive ELA mice to make decisions largely relying on habit memory, while loss of small synapses in hippocampal CA1 damage the spatial memory of ELA mice.

Wedge Movement Effects on the Nonlinear Behavior of an Arch Dam Subjected to Seismic Loading

Arch dam abutment stability is regarded as a prominent part of designing concrete dams. In the present study, the seismic stability of the rock wedges in the foundation of the concrete arch dams is studied using limit state and finite-element methods. For this purpose, a double-curvature arch dam (the Bakhtiari dam), is taken into consideration. Each abutment has six wedges. The finite-element model (FEM) includes the dam, reservoir, wedges, and the rest of the foundation. The dam abutment seismic safety is analyzed using the time history analysis along with the Londe method. Subsequently, the wedges in the proximity of the critical state are identified. Afterward, the analysis of the critical wedges’ joints has been conducted via FEM, and the damage propagation in the dam body has been investigated. Moreover, the effects of the grout curtain efficiency, hazard level, and foundation flexibility on the nonlinear behavior of the arch dam with integrated foundation and foundation with joints are scrutinized. The outcomes reveal the remarkable effect of joints considered in the foundation on the dam behavior.

A multiple-point monitoring model for concrete dam displacements based on correlated multiple-output support vector regression

Displacements reflect the overall behavior of a concrete dam; thus, it is of vital importance to evaluate the overall structural health status by displacement-based mathematical monitoring models. However, most of the existing monitoring models focus on point-by-point displacement modeling, ignoring the correlations among displacements at different measurement points. This study therefore proposes a model for dam multiple-point displacement monitoring based on the support vector regression (SVR) algorithm. The improved SVR-based model with multiple-output formulation is a new development based on the statistical learning theory, which can simultaneously analyze and predict displacements at multiple-measurement points. Furthermore, by introducing the weight vectors that separate the common and individual information, the potential correlations among multiple-point displacements can be fully exploited by the multiple-output SVR. Combining the above two improvements, a multiple-point monitoring model for dam displacements considering spatiotemporal correlations, referred to as correlated multiple-output SVR (CMOSVR), is constructed. The proposed model is verified using in-situ monitoring from a full-scale concrete gravity dam. The accuracy, robustness, and efficiency of the CMOSVR-based model are compared with those of conventional single-point monitoring models, such as classical hydrostatic-seasonal-time model and standard SVR-based model. Empirical results show that in both real and simulated noisy scenarios, the CMOSVR-based multiple-point model can achieve a better monitoring performance with less modeling time cost. Moreover, the superior performance of CMOSVR-based model does not require a very strong correlation among multiple-point displacements, which considerably improves the adaptability of the monitoring model to various possible scenarios. The novel multiple-point model will provide an effective technical support tool for ensuring the safe operation of dams.

Lightweight, wireless LED implant for chronic manipulation in vivo of spontaneous activity in neonatal mice

BackgroundLong-term manipulation of activity in the neonatal rodent brain can help us understand healthy development, but also involves a set of challenges unique to the neonatal animal. As pups are small, cannot be separated from their mother for long periods of time, and must be housed in a nest, many traditional techniques are unusable during the first two postnatal weeks. New methodHere, we describe the use of magnetic resonance induction to allow wireless and chronic optogenetic manipulation of spontaneous activity in mouse pups during the second postnatal week. ResultsPups were implanted with a lightweight receiver coupled to an LED and successfully returned to the homecage. A transmitter coil surrounding the homecage drove the implanted LED and was regulated by a microcontroller to allow flexible, precisely-timed and wireless control over neuronal manipulation. In vivo patch-clamp recordings verified that activation of the LED triggered bursts of action potentials in layer 2/3 neurons that expressed channelrhodopsin-2 in the visual cortex without directly affecting neighboring, non-expressing neurons. The implants are stable and functional for at least 10 days and do not have an impact on the weight gain of pups. Implanted pups’ behavior is mildly affected only briefly after surgery, while maternal behavior of dams remains unaffected. Comparison with existing method(s)In contrast to most other methods for wireless optogenetic stimulation, the small size and low weight of the receiver allow complete implantation in animals that are as small as a newborn mouse. ConclusionsThis method is ideal for investigating the function and development of cortical circuits in small and developing animals. Furthermore, our method is economical and easy to adapt to diverse experimental designs.

Determination of 3D near fault seismic behaviour of Oroville earth fill dam using burger material model and free field-quiet boundary conditions

ABSTRACT In this study, the three-dimensional (3D) near-fault earthquake performance of the Oroville dam is examined considering a special material model and various seismic boundary conditions. The 3D finite-difference model of the Oroville EF dam is modeled using the finite difference method. Burger Creep (BC) material model is utilized for the foundation and dam body materials. Special interface elements are taken into account between the dam body and foundation. Fix, free field, and quiet seismic boundary conditions are considered for 3D nonlinear earthquake analyses. Total six various strong near-fault earthquakes are used in the 3D analyses. According to the non-linear earthquake analyses, principal stresses, horizontal and vertical displacements for three nodal points are assessed in detail and numerical results are compared for reflecting and non-reflecting seismic boundary conditions. It is clearly understood that seismic boundary conditions should not be utilized randomly for 3D modeling and analysis of EF dams.

Thermal Analysis of Roller Compacted Concrete Dam Utilizing a Probabilistic Model

Roller compacted concrete (RCC) dam safety evaluation requires a realistic definition of thermal condition during construction and operation. Seasonal variations of temperature induce significant tensile stress. Therefore, the required preparations should be made to control and limit the thermal stresses. In this study, the effect of thermal and mechanical parameters on RCC dam behavior is evaluated using a probabilistic model. ANSYS Workbench software based on the finite element method has been utilized. The Latin hypercube sampling (LHS) method was used for probabilistic and sensitivity analysis, in which the density of concrete, Young's modulus, Poisson ratio, thermal conductivity, and specific heat have been selected as input variables. Dam body temperature, total deformation, and maximum principal stress in the body were considered as output variables. To evaluate the structural performance of the case model of the RCC dam under thermal loading, the model sensitivity to selected input variables is investigated. Considering to obtained curves, it can be concluded that the mechanical and thermal parameters have different effects on the performance of the RCC dam under variation of body temperature. Also, the results show that the sensitivity curves of output parameters are linear or bilinear relative to input variables.

3D Finite Element Analysis of a Concrete Dam Behavior under Changing Hydrostatic Load: A Case Study.

In this study, a large arch-gravity Moste Dam was analyzed, where an automated system for the measurements of horizontal displacements of the upper part of the dam was established. Two-dimensional (2D) and three-dimensional (3D) analyses of dam behavior, taking into account the earth pressures and the hydrostatic load, using the finite element method (FEM)-based computer program DIANA, were performed. The influence of lowering the water level of the reservoir by 6.2 m, on the horizontal displacements of the upper part of the dam, at stationary temperature conditions, was investigated. It was found that the results of the performed 2D and 3D FEM analyses fitted in very well with the result of experimentally determined measurement of horizontal displacements (which was 0.48 mm in the upstream direction) that was obtained using a hanging pendulum. An additional comparison of the results of 3D calculations showed that the finite element mesh density had a small effect on the calculated horizontal displacements.