Behavior Of Dams Research Articles

Comparison of K-Means and FCM Algorithms to Optimize Spatiotemporal Pore Pressure Prediction of Earth Dams

To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (FCM) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the FCM clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, FCM clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.

Granular Soils and Contaminant Modeling in Tailing Dams

The granular soils of tailings, encompassing clay, gravel, sand, and silt, play a pivotal role in the behavior and stability of tailings dams. Different types of granular soils significantly influence the tailings material’s strength, compressibility, and permeability. This study highlights the importance of understanding the relationship between soil types and contaminant properties when analyzing solute transport through numerical modeling. Consequently, various soil types were incorporated into the initial tailings dam model to simulate contaminant transport based on solute transport analysis. The findings underscored the essential role of granular soils in contaminant dispersion within tailings dams. Finer particles, such as clay and silt, demonstrated higher adsorption capacities, which slow contaminant movement. In contrast, coarser materials, like sand and gravel, enable faster transport, increasing the potential for rapid dispersion.

Perinatal running training reversed postnatal anxiety and depressive-like behavior and cognitive impairment in mice following prenatal subchronic variable stress

BackgroundPregnancy is a very complex and highly stressful time in women. 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. Regular physical activity has 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 perinatal running training can affect maternal care stress-related anxiety, depressive-like behavior, and cognitive changes in postpartum dams and to explore the possible underlying mechanism. Methods40 female C57BL/6J mice were divided into four groups: prenatal control (NC) and running training (EX) group (NC+EX), prenatal control and nonrunning training (RE) group (NC+RE), prenatal subchronic variable stress (SCVS) and running training group (SCVS+EX) and prenatal SCVS and non-running training group (SCVS+RE). Mice in prenatal stress groups were subjected to SCVS after pregnancy confirmed. Mice in running training groups subjected to running training throughout pregnancy and lactation. Then after the delivery, maternal behavior, cognitive changes, anxiety and depressive-like behaviors were tested. Then we measured the serum prolactin (PRL), hypothalamic–pituitary adrenal (HPA) axis activity, and adult hippocampus neurogenesis (AHN) in dams. ResultsCompared to NC+RE, prenatal SCVS caused cognitive impairments, the decrease in maternal behavior, and anxiety and depressive-like behavior in SCVS+RE dams, accompanying increase in HPA axis activity and decreased the PRL levels and AHN in postpartum period. Then compared to SCVS+RE, perinatal running training mitigates cognitive impairments, increased maternal behavior, and alleviates anxiety and depressive-like behavior in SCVS+EX dams, accompanying the decreased HPA axis activity, and the increased PRL levels and AHN in postpartum period. ConclusionOverall, this study suggests that perinatal running training might improve maternal care and reverse prenatal stress-related cognitive impairment and anxiety and depressive-like behavior in postpartum dams.

Onsite Seismic Monitoring Behavior of Undamaged Dams During the 2023 Kahramanmaraş Earthquakes (M7.7 and M7.6).

On 6 February 2023, two major earthquakes struck Türkiye, with their epicenters in the Pazarcık (M7.7; focal depth: 8.6 km) and Elbistan (M7.6; focal depth: 7 km) districts of Kahramanmaraş city. Most of the dams in the earthquake region remained structurally safe and stable. However, 17 dams in Türkiye and 1 dam in Syria were damaged during the 2023 Kahramanmaraş earthquakes. The main objective of this study was to better understand the real seismic behaviors of the dams during the two mainshocks and significant aftershocks. An earthfill dam, a concrete-faced rockfill dam (CFRD), and a roller-compacted concrete (RCC) dam constructed in the disaster area were selected to identify the real seismic behaviors of different types of dams during strong earthquakes. Acceleration records measured at the crest, right and left abutments, and foundations of the selected dams during the 2023 Kahramanmaraş earthquakes were taken into account to determine the real seismic behavior of the dams before, during, and after the earthquakes. The results of this investigation provide valuable insights into the real seismic behaviors of different types of dams in the vicinity of fault lines during strong earthquakes.

Analysis and monitoring of the behavior of a rock fill dam ten years after construction: a case study of the Iran-Madani Dam

AbstractIn this study we compared dam monitoring results with those of numerical analysis to propose a plan for the first reservoir impounding of the Iran-Madani Rock fill dam, ten years after the completion of its construction. The stability of the dam body has been assessed using numerical analysis and data obtained from sensors installed in the dam. The correctness and accuracy of the geotechnical parameters of the dam body materials were confirmed by comparing the results of numerical analysis and monitoring through back analysis. The linear correlation coefficients between the experimental data and the numerical results for settlement, pore water pressure, and total stress are 84%, 67%, and 99%, respectively. In addition, the agreement between the design assumptions with both the numerical analysis results and instrumentation data was examined. The arching ratio values obtained from instrumentation and numerical analysis in the core of the dam are 0.47 and 0.35, respectively, indicating the safety of the dam. Finally, a numerical sensitivity analysis was conducted to present a special impounding program for the dam, with a focus on controlling simultaneous changes in pore water pressure and effective stress in the clay core, ten years after the completion of the dam body construction.

Efficient prediction uncertainty quantification in dam behavior monitoring with attention-based sequence-to-sequence learning

Displacement is an intuitive monitoring indicator of dam structural behavior. Conventional deterministic modeling methods frequently disregard the inherent uncertainty associated with monitoring data. This study aims to quantify such uncertainties within dam displacement predictions by augmenting a sequence-to-sequence structure with the novel recurrent unit, termed as tiny gated unit (TGU), attention mechanism, as well as quantile regression, resulting in the Att-S2STQ model. Specifically, TGU adopts only one gate to identify nonlinearity in data sequences, while Bahdanau attention mechanism dynamically assigns weights to different parts of the input sequence. The inclusion of quantile regression allows Att-S2STQ to produce prediction intervals (PIs), from which the probability density functions (PDFs) and cumulative distribution functions (CDFs) are further derived via kernel density estimation. PIs, PDFs, and CDFs jointly serve to reveal the prediction uncertainty. The effectiveness of the model is illustrated through comparative experiments using real-world dam monitoring datasets. Results indicate that the proposed model outperforms traditional methods in point, interval, and probability predictions, while also having a simpler structure and faster training. The superiority of both accuracy and efficiency makes it a valuable tool for dam management, aiding in data-driven decision-making and enhancing operational safety.

Elastic-plastic-creep behavior of high rockfill dams: a case study on Lianghekou CRFD

The instantaneous loading and time-dependent creep deformations occur simultaneously during the lifetime of rockfill dams, but their proportions in monitoring records are still unclear. In previous works, loading and creep were generally simulated independently in turn. This uncoupled method may induce misleading conclusions in interpreting dam behaviors. In this paper, an advanced elastic-plastic-creep model is applied to capture the interaction of loading and creep under complex conditions with varying loading rates based on a benchmark example of 295m-high Lianghekou core wall rockfill dam (CRFD). The instantaneous elastic-plastic parameters of rockfills are calibrated by large-scale triaxial tests (including 300mm and 800mm in diameter) and the time-dependent creep parameters are obtained by back analyses on monitoring records. A series of numerical analyses on the dam are conducted. The loading-creep coupling process of rockfills is successfully captured and the instantaneous and time-dependent creep deformations are explicitly separated. The 800mm-diameter triaxial test weakens the size effect and provides an acceptable representation for the instantaneous behaviors of prototype rockfills. The good agreement between the calculations of the elastic-plastic-creep coupling analysis and measurements indicates creep contributes about 8~15% to total construction deformation. The work provides guides for properly interpreting and evaluating actual dam behaviors.

Maternal stress during pregnancy alters circulating small extracellular vesicles and enhances their targeting to the placenta and fetus

BackgroundMaternal psychological distress during pregnancy can negatively impact fetal development, resulting in long-lasting consequences for the offspring. These effects show a sex bias. The mechanisms whereby prenatal stress induces functional and/or structural changes in the placental-fetal unit remain poorly understood. Maternal circulating small extracellular vesicles (sEVs) are good candidates to act as “stress signals” in mother-to-fetus communication. Using a repetitive restraint-based rat model of prenatal stress, we examined circulating maternal sEVs under stress conditions and tested whether they could target placental-fetal tissues.ResultsOur mild chronic maternal stress during pregnancy paradigm induced anhedonic-like behavior in pregnant dams and led to intrauterine growth restriction (IUGR), particularly in male fetuses and placentas. The concentration and cargo of maternal circulating sEVs changed under stress conditions. Specifically, there was a significant reduction in neuron-enriched proteins and a significant increase in astrocyte-enriched proteins in blood-borne sEVs from stressed dams. To study the effect of repetitive restraint stress on the biodistribution of maternal circulating sEVs in the fetoplacental unit, sEVs from pregnant dams exposed to stress or control protocol were labeled with DiR fluorescent die and injected into pregnant females previously exposed to control or stress protocol. Remarkably, maternal circulating sEVs target placental/fetal tissues and, under stress conditions, fetal tissues are more receptive to sEVs.ConclusionOur results suggest that maternal circulating sEVs can act as novel mediators/modulators of mother-to-fetus stress communication. Further studies are needed to identify placental/fetal cellular targets of maternal sEVs and characterize their contribution to stress-induced sex-specific placental and fetal changes.

Verification, validation, and uncertainty quantification (VVUQ) in structural analysis of concrete dams

Over the past three decades, advancements in computational power and numerical methods have significantly enhanced the role of structural analyses in the design and safety assessment of dams. Simulating concrete dam behavior, particularly in interactions with reservoir water and rock foundations, poses formidable computational challenges. Additionally, the need to define uncertainties related to material parameters, loading conditions, and modeling strategy adds complexity to the modeling process, therefore, quantifying sources of uncertainty is crucial for maintaining credibility and confidence in analysis results. This paper provides a synthesis and an overview of existing research and presents a generic framework for evaluating the credibility of advanced structural analysis methods for concrete dams, with a focus on their limitations and associated uncertainties. The methodology includes a comprehensive process for structural analysis, verification, validation, and uncertainty quantification, aiming to facilitate condition assessments of concrete dams.

Deep Neural Networks for Predicting the Settlement of Earth Dams Based on the InSAR Outputs

Earth dams play a crucial role in water resource management, necessitating effective maintenance to ensure prolonged functionality and safety. Monitoring these dams traditionally involves methods such as surveying and instrumentation. However, challenges arise from equipment malfunctions and absences, especially in dams affected by environmental changes. In response to these challenges, the cost-effective and adaptable nature of Interferometric Synthetic Aperture Radar (InSAR) has made it a preferred choice for monitoring. Despite their advantages, traditional numerical models like finite element methods have limitations in predicting deformation comprehensively, particularly due to intricate, non-linear correlations involving material type and environmental conditions. To overcome these limitations, this research employs deep learning techniques, specifically Long Short-Term Memory (LSTM) network, to capture intricate relationships and accurately predict dam behavior. Time series data from InSAR, representing settlement, are decomposed into trend and seasonal components using Artificial Neural Networks (ANN) for trend prediction. Furthermore, an LSTM network is utilized to handle the complexity of the seasonal component and its correlation with environmental factors. This network incorporates settlement, precipitation, temperature, and reservoir water level time series as inputs, thereby enhancing prediction accuracy. The research outcome presents a robust solution that holds the promise of increased accuracy and efficiency in predicting, monitoring, and serving as an early warning system for earth dam deformations over time. Such advancements are crucial for ensuring the safety and integrity of critical infrastructure in the face of evolving environmental conditions.

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.

Analysis of Fill Dam Using Finite Element Method and Comparison with Monitoring Results

Nowadays, a detailed safety policy is applied for dams. These policies cover structural safety, monitoring, inspection, safe operation, and emergency plans. For high-risk dams, all these policy elements need to be included in dam safety programs. Deficiencies in embankment dams, which suffer the most damage, can be detected by visual inspection and programmed monitoring of dams. In dams, horizontal and vertical deformation, leakage, pressure, stress, loads acting on structural elements, and environmental factors are generally measured. These behaviors can be numerically modeled to determine the dam behavior. Numerical analysis methods are important for monitoring the safety of the dam. Models created with software such as Plaxis provide information about dam behavior. Although numerical analysis is very important for dams, obtaining the material parameters used in the construction of the dam needed for modeling, recording the construction stages of the dam, not taking the water level change in the dam reservoir instantaneously, and not taking the measurement records of the dam measurement instruments correctly for different reasons constitute problems and difficulties for the analyses. Within the scope of this study, İkizdere Dam in Turkey was modeled with the Plaxis finite element program; the survey and piezometer measurement data taken from the dam were evaluated by comparing with the analysis results; the difficulties and problems encountered in the modeling and analysis phase were stated, and recommendations were made on dam safety and numerical analysis. Thus, in addition to other studies, it was emphasized that it is important for dam engineers to monitor the use of numerical analysis models throughout the entire process, not only in the planning phase but also from the planning phase to the life of the dam, and to keep records of all recording intervals that will be needed in digital analysis models.

A simplified method for predicting overflow-induced crack propagation in gravity dams using genetic algorithm and material-based model

Cracks are always a serious concern in the stability analysis of gravity dams. One of the main reasons for the initiation of cracks is overflows. In evaluating dam safety under flood conditions, it is crucial to consider the balance between the strength of the dam body and the propagation of cracks. In this context, simplified equations serve as valuable tools in dam design as they offer a quick and efficient way to estimate the behavior of the dam structure. In this study, firstly using a polygonal material-based model (P-MBM), which is improved in considering the softening behavior of joints, the micro-crack propagation in masonry material is investigated. Then, based on performing 81 numerical simulations in a parametric study on the geometrical and geotechnical properties of the dam subjected to overflow, a great database of the behavior of different dams is investigated. Then, using the genetic algorithm, a set of equations is proposed, and their accuracy is validated through UDEC simulations and some theoretical methods from existing literature. For all models, the crack tends to be horizontal at the initial phase. Then, due to increasing the compressive stresses on the downstream side, the fractures tend to the dam toe. The results also indicate that the proposed equations can reasonably determine the behavior of gravity dams and the development of cracks in the dam body. The outcomes highlighted the considerable effects of geometry and geotechnical properties on dictating the trajectory of crack growth.

Predicting the Dynamic Behaviour of a Concrete Dam using Statistical and Machine Learning Models

Operational Modal Analysis is a reliable methodology for the assessment of civil engineering structures, allowing for the accurate definition of their dynamic behavior. Additionally, since it does not require the use of artificial excitation, it becomes a cost-effective choice for the performance of singular tests, as well as a consistent option for the long-term continuous monitoring of structures. Nevertheless, the modal characteristics of structures are affected by environmental and operational conditions, concealing the variations that could emerge due to abnormal behavior. With respect to concrete dams, factors such as temperature and the level of water in the reservoir exert a pronounced influence in the evolution of natural frequencies, increasing data variability and camouflaging the behavior that would be discerned under stable conditions. In this context, the current study seeks to examine the ability of statistical and machine learning tools to mitigate the effects of external conditions on the modal properties of concrete dams, specifically natural frequencies. To achieve this objective, the efficiency of methods incorporating measurements of variables impacting the structure, such as Multiple Linear Regressions and Neural Networks, is compared to that of tools not needing these inputs, as is the case of Principal Components Analysis and the Minimum Mean Square Error estimator. Experimental data obtained during the continuous dynamic monitoring of a concrete dam in Portugal is used as a case study.

Dynamic response of Akcakoca RCC Dam including galleries

Dams are structures that demand a significant amount of concrete, making them costly constructions. Hence, various alternative methods are employed for dam construction, one of which is the use of Roller Compacted Concrete (RCC). This is a case study, which focuses on examining the behavior of RCC dams with and without galleries under seismic excitations, considering the Akçakoca RCC dam in Düzce Province. The seismic ground motion data from the 1999 Düzce earthquake were utilized. The impact of galleries inside the concrete dams was investigated through stress and displacement, considering both empty and full reservoir conditions. The Eulerian-Lagrangian coupled (CEL) approach was employed for two-dimensional fluid finite elements. This study revealed that reservoir water and galleries have a notable influence on the behavior of dams under seismic forces. In cases with reservoirs and galleries, stress and displacement values increased, and critical changes in stresses were observed around gallery areas. Therefore, careful design considerations are essential for dams with galleries under significant seismic forces.

A novel method for settlement imputation and monitoring of earth-rockfill dams subjected to large-scale missing data

Various missing values inevitably exist in the settlement monitoring data of prolonged operation of earth-rockfill dams, which delays or even interrupts the dam structure analytical procedure. This study aims to develop a reliable method for addressing the missing data problem and monitoring earth-rockfill dam settlement behavior. First, an imputation model, support vector regression based on the finite element method and time input (FEMTSVR), is proposed and offers superior performance when capturing complex nonlinear mappings from environmental variables to settlement on small sample data. Herein, an improved particle swarm optimization algorithm (IPSO) is developed, realizing the nonlinear adjustment of weights and parameter reduction during hyperparameter optimization. Then, this study establishes a sequential prediction model based on gate recurrent unit (GRU) networks to monitor dam behavior on the imputed and complete dataset. Eventually, the proposed method is evaluated using real-world earth-rockfill dam monitoring data with the help of statistical indicators, demonstrating its efficiency in monitoring settlement data subjected to large-scale missingness. This study provides a robust database for dam structural health monitoring, while also providing a promising framework for the safety assessment of other civil or hydraulic engineering based on raw monitoring data.

Early Handling Exerts Anxiolytic Effects and Alters Brain Mitochondrial Dynamics in Adult High Anxiety Mice

Early handling (EH), the brief separation of pups from their mother during early life, has been shown to exert beneficial effects. However, the impact of EH in a high anxiety background as well as the role of brain mitochondria in shaping EH-driven responses remain elusive.Here, we used a high (HAB) vs. normal (NAB) anxiety-related behavior mouse model to study how EH affects pup and dam behavior in divergent anxiety backgrounds. We also investigated EH-induced effects at the protein and mRNA levels in adult male HAB mice in the hypothalamus, the prefrontal cortex, and the hippocampus by examining the same mitochondrial/energy pathways and mitochondrial dynamics mechanisms (fission, fusion, biogenesis, and mitophagy) in all three brain regions.EH exerts anxiolytic effects in adult HAB but not NAB male mice and does not affect HAB or NAB maternal behavior, although basal HAB vs. NAB maternal behaviors differ. In adult HAB male mice, EH does not impact oxidative phosphorylation (OXPHOS) and oxidative stress in any of the brain regions studied but leads to increased protein expression of glycolysis enzymes and a correlation of anxiety-related behavior with Krebs cycle enzymes in HAB mice in the hypothalamus. Intriguingly, EH alters mitochondrial dynamics by increasing hypothalamic DRP1, OPA1, and PGC1a protein levels. At the mRNA level, we observe altered, EH-driven mitochondrial dynamics mRNA signatures which predominantly affect the prefrontal cortex.Taken together, our results show that EH exerts anxiolytic effects in adulthood in high anxiety and modulates mitochondrial dynamics pathways in a brain region-specific manner.

A multisource data‐driven monitoring model for assessing concrete dam behavior

AbstractThe pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.

Presenting the AI models in predicting the settlement of earth dams using the results of spatiotemporal clustering and k-means algorithm

In this work, the results of instrumentation over 8 years, including the phases of construction, first impounding, and operation, have been used to analyze the location of the Eyvashan Dam settlement. Mohr–Coulomb behavioral model and numerical model of Plaxis 2D software were used to verify the monitoring results. The results demonstrated that settlement of the dam has increased in the dam's core since the beginning of construction, and they eventually stabilized during the operation phase. After the completion of the construction phase, the maximum settlement of the dam core was recorded as 809 mm, which is equivalent to 1.2% of the height of the dam at the middle level. Also, an approach to interpreting the settlement behavior of earth dams has been presented that is based on spatiotemporal clustering. Also, RF, MARS, and GMDH models were created based on a proposed scenario to predict settlement using points located in a cluster. Therefore, the settlement location of the studied dam was determined using the results of the k-means clustering algorithm in the aforementioned AI models. The high accuracy of the results of the proposed method confirms the proper performance of using AI models in predicting and diagnosing the settlement of earthen dams using the results of k-means spatiotemporal clustering algorithm. The evaluation of the models shows that the ENN model is a more suitable and efficient tool in this field and can be useful in monitoring the settlement of earth dams.

Changes in Cerebral Mitochondrial Function in Postpartum Dams Exposed to a Low-resource Environment during Weaning

Background. Postpartum depression is a serious mental-health condition, affecting over 14% of all mothers in the U.S. Poverty, a lack of educational and economic resources, is a major determinant of adult mental health. Within the U.S., poverty impacts 11% of adults, with a greater incidence in women of childbearing age (roughly 16%) and postpartum women (roughly 14%). While pregnancy and poverty separately increase risks of developing depression, few studies have combined these conditions to determine their role in the development of depression. Further, no studies have explored mechanisms contributing to depression following pregnancy and poverty. However, cerebral mitochondrial dysfunction (C-mtDys) may be one mechanism. The aim of this study is to examine C-mtDys in postpartum (PP) dams exposed to the limited-bedding-nesting (LBN) model in rats, which reduces their nesting material to simulate a low-resource environment. We hypothesize that PP dams exposed to the LBN model will exhibit C-mtDys and elevated oxidative stress. Methods. Pregnant Sprague-Dawley rats gave birth naturally and were divided randomly into LBN (n =2) or control (n = 2) groups. LBN dams were exposed to the LBN model from PD 2 through PD 9. To validate the success of the LBN model, entropy scores, which are a measure of behavior unpredictability, were recorded for each dam. At 17 weeks PP, equivalent to 8 years PP in humans, brains were collected and used to isolate mitochondria through differential centrifugation. Mitochondrial respiratory states were evaluated via respiration (mtRes) to determine function. Oxidative stress in the whole brain was examined through H2O2 and total antioxidant capacity colorimetric biochemical assays. Results. LBN PP dams displayed higher entropy scores (1.10 ± 0.04 v. 0.79 ± 0.04, p < 0.05), as expected based on prior literature, serving as validation of the model. At 17 weeks, PP LBN dams had reduced mtRes in all states, including the basal state (176.09 ± 5.90 v. 360.70 ± 7.73 pmol/s/mg, ns), State 2 (1044.72 ± 14.00 v. 1703.00 ± 18.10 pmol/s/mg, ns), State 3 (3843.27 ± 31.86 v. 6705.72 ± 37.54 pmol/s/mg, ns) and State 4 (776.83 ± 13.65 v. 1533.54 ± 16.46 pmol/s/mg, ns). PP LBN dams demonstrated reduced total antioxidant capacity by roughly 20% (54.10 ± 1.24 v. 63.04 ± 0.99 mM Trolox, ns) and elevated H2O2 by roughly 50% (2.12 ± 0.52 v. 1.35 ± 0.28 nM/mg, ns). Summary. In summary, preliminary data shows C-mtDys via reduced mtRes in PP dams exposed to an impoverished environment during weaning. Furthermore, this study suggests that decreased mtRes could contribute to increased oxidative stress in the brain. Elevated oxidative stress may cause damage at the cellular and circuitry levels in the brain that could facilitate the development of depression later in life. Future studies will further examine C-mtDys, oxidative stress, and depressive behaviors in PP dams exposed to LBN. This study is significant because it identifies C-mtDys as a possible mechanism causing depression after exposure to both pregnancy and poverty. This project was funded by the American Heart Association Early Career Development Award [AHA 18CDA34110264 (Cunningham)]. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.