A damage model for structures with degrading response

The paper presents a hysteretic damage model for the response simulation of structural components with strength and stiffness deterioration under cyclic loading. The model is based on 1D continuum damage mechanics and relates any 2 work‐conjugate response variables such as force‐displacement, moment‐rotation, or stress‐strain. The strength and stiffness deterioration is described by a continuous damage variable. The formulation uses a criterion based on the hysteretic energy and the maximum or minimum deformation for damage initiation with a cumulative probability distribution function for the damage evolution. A series of structural component response simulations showcase the ability of the model to describe different types of hysteretic behavior. The relation of the model's damage variable to the Park‐Ang damage index is also discussed. Because of its consistent and numerically robust formulation, the model is suitable for the large‐scale seismic response simulation of structural systems with strength and stiffness deterioration.     

A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India

In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling.     

Seismotectonic database of Turkey

Turkey is located in one of the most seismically active regions in the world. Characterizing seismic source zones in this region requires evaluation and integration of geological, geophysical, seismological and geodetical data. This first seismotectonic database for Turkey presented herein was prepared, under the framework of the National Earthquake Strategy and Action Plan—2023. The geographic information system (GIS)-based database includes maps of active faults, catalogues of instrumental and historical earthquakes, moment tensor solutions and data on crustal thickness. On the basis of these data, 18 major seismotectonic zones were delineated for Turkey and the surrounding region. The compilation and storage of the seismotectonic data sets in a digital GIS will allow analyses and systematic updates as new data accrete over time.     

Effects of the environment on fish juvenile growth in West African stressful estuaries

The knowledge of juvenile fish growth in extreme environmental conditions is a key to the understanding of adaptive responses and to the relevant management of natural populations. The juvenile growth of an extreme euryhaline tilapia species, Sarotherodon melanotheron (Cichlidae), was examined across a salinity gradient (20–118) in several West African estuarine ecosystems. Juveniles were collected during the reproduction period of two consecutive years (2003 and 2004) in six locations in the Saloum (Senegal) and Gambia estuaries. Age and growth were estimated using daily otolith microincrements. For each individual, otolith growth rates showed three different stages (slow, fast, decreasing): around 4 ± 0.5 μm d⁻¹ during the first five days, 9 ± 0.5 μm d⁻¹ during the next 15 days and 4 ± 0.50 μm d⁻¹ at 60 days. Growth modelling and model comparisons were objectively made within an information theory framework using the multi-model inference from five growth models (linear, power, Gompertz, von Bertalanffy, and logistic). The combination of both the model adjustment inspection and the information theory model selection procedure allowed identification of the final set of models, including the less parameterised ones. The estimated growth rates were variable across spatial scales but not across temporal scales (except for one location), following exactly the salinity gradient with growth decrease towards the hypersaline conditions. The salinity gradient was closely related to all measured variables (condition factor, mean age, multi-model absolute growth rate) demonstrating the strong effect of hypersaline environmental conditions—induced by climate changes—on fish populations at an early stage.     

Comparison of Turbulent Flows and Suspended Sediment Particle Motions Simulated around a Submerged Breakwater Using RANS and LES

Beach erosion is a serious problem that can be aggravated by human-made structures, and the modeling of breaking waves near the coast and around coastal st     

Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques

Air over-pressure (AOp) is one of the products of blasting operations for rock fragmentation in open-pit mines. It can cause structural vibration, smash glass doors, adversely affect the surrounding environment, and even be fatal to humans. To assess its dangerous effects, seven artificial intelligence (AI) methods for predicting specific blast-induced AOp have been applied and compared in this study. The seven methods include random forest, support vector regression, Gaussian process, Bayesian additive regression trees, boosted regression trees, k-nearest neighbors, and artificial neural network (ANN). An empirical technique was also used to compare with AI models. The degree of complexity and the performance of the models were compared with each other to find the optimal model for predicting blast-induced AOp. The Deo Nai open-pit coal mine (Vietnam) was selected as a case study where 113 blasting events have been recorded. Indicators used for evaluating model performances include the root-mean-square error (RMSE), determination coefficient (R²), and mean absolute error (MAE). The results indicate that AI techniques provide better performance than the empirical method. Although the relevance of the empirical approach was acceptable (R²?=?0.930) in this study, its error (RMSE?=?7.514) is highly significant to guarantee the safety of the surrounding environment. In contrast, the AI models offer much higher accuracies. Of the seven AI models, ANN was the most dominant model based on RMSE, R², and MAE. This study demonstrated that AI techniques are excellent for predicting blast-induced AOp in open-pit mines. These techniques are useful for blasters and managers in controlling undesirable effects of blasting operations on the surrounding environment.

     

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost

Natural Resources Research - Blasting is a useful technique for rocks fragmentation in open-pit mines, underground mines, as well as for civil engineering work. However, the negative impacts of...     

Determination of the tectonic evolution of the Edremit Gulf based on seismic reflection studies

The Edremit Gulf, which developed during the Neogene-Quaternary, is a seismically active graben in NW Anatolia (Turkey) surrounded by the Sakarya continent. The sedimentary deposits in the gulf overlie the bedrock unconformably and can be separated into two parts as upper and lower deposits based on similarity of their seismic characteristics, and because the contact between them is clear. The lower deposits are characterized in the seismic profiles by the absence of well defined, continuous reflectors and are strongly disturbed by faults. A tectonic map and structural model of the Edremit Gulf was derived from interpreting 21 deep seismic profiles trending NE–SW and NW–SE within the gulf. Two fault systems were distinguished on the basis of this compilation. The NNW–SSE trending parallel faults are low-angle normal faults formed after compression. They controlled and deformed the lower basin deposits. A syncline and anticline with a broad fold-curvature length resulted in folds that developed parallel to basin boundaries in the lower basin deposits. The ENE–WSW trending high-angle faults have controlled and deformed the northern basin of the Edremit Gulf. The folds developed within the northern lower deposits originated from the listric geometry of the faults. These faults are normal faults associated with regional N–S extension in western Anatolia. The Edremit Gulf began to open under the control of low-angle NNW–SSE trending faults that developed after the compression of western Anatolia in an E–W direction in the early Neogene. Subsequently, regional N–S extensional stress and high-angle normal faults cut the previous structures, opened the northern basin, and controlled and deformed the lower basin deposits in the gulf. As a result, the Edremit Gulf has not been controlled by any strike-slip faults or the Northern Anatolian Fault. The basin developed in the two different tectonic regimes of western Anatolia as an Aegean type cross-graben from the Neogene to Holocene.     

Using stable isotopes to estimate young water fractions in a heavily regulated,tropical lowland river basin

The young water fraction of streamflow (Fyw), an important hydrological variable, has been calculated for the first time, for a monsoon-fed coastal catchment in northern Vietnam. Oxygen stable isotopes (δ¹⁸O) from six river sites in the Day River Basin (DRB) were analysed monthly, between January 2015 and December 2018. River δ¹⁸O signatures showed sine wave variability, reflecting the amount effect and tropical (dry-rainy) seasonality of the region. The δ¹⁸O composition of precipitation ranged from −12.67 to +1.68‰, with a mean value of −5.14‰, and in-streamflow signatures ranged from −11.63 to −1.37‰ with a mean of −5.02‰. Fractions of young water (Fyw) were calculated from the unweighted and flow-weighted δ¹⁸O composition of samples. Unweighted Fyw ranged between 29 ± 8% and 82 ± 21% with a mean value of 51 ± 19%, and was not significantly different from flow-weighted Fyw (range between 33 ± 25% and 92 ± 73%, mean 52 ± 36%). Both unweighted and flow-weighted Fyw were highest in the middle of stream and lowest in downstream sites, capturing the impacts of landuse changes, hydrology and human activities in the catchment. Our calculations imply that more than a half of rainwater reaches the DRB river mainstream within the first 3 months. The Fyw is much higher than the global average (of one-third) and insensitive to discharge due to the combination of a humid catchment with high rainfall, low storage capacity, flat landscape and an intensive drainage system in the DRB. Also the low discharge sensitivity of Fyw in the DRB implies that the regional hydrology is severely altered by humans.     

Modeling soil desiccation cracking by analytical and numerical approaches

An energy approach is proposed as a complement to the stress approach commonly considered for investigating soil desiccation cracking. The elastic strain energies before and after crack initiation are estimated by both numerical and analytical solutions. The energy released by cracking is then compared with the fracture energy to discuss crack initiation conditions. This leads to combined energy and stress conditions for crack initiation following Leguillon's theory. An approximate analytical solution is derived from a variational formulation of the porous elastic body equations. A cohesive zone model and finite element code are used to simulate crack propagation in an unsaturated porous body. This analysis shows that the energy criterion is reached before the stress criterion, and this can explain unstable crack propagation at the beginning. The approximate analytical solution allows predicting correctly the crack depth and opening in its initiation stage.