Journal of Numerical Methods in Civil Engineering
Volume:8 Issue: 2, Dec 2023

This paper numerically studies the effect of the width-to-thickness ratio of inclined strips on the behavior of a novel slotted steel plate shear wall (SPSW). The slotted SPSW consists of horizontal and vertical boundary elements (BEs) and two inclined-slotted plates (ISPs) connected by high-strength steel bolts. The directions of the slots in each infill plate are opposite. Steel bolts are used to connect the two infill plates through the created holes at the intersection of each inclined slot. This paper numerically examined four slotted steel shear walls with different width-to-thickness ratios of strips. The research showed that when the slotted steel shear walls were put under cycling loading, the inclined steel strips on one side of the wall were placed in tension; however, the strips on the other side undoubtedly were in compression. Additionally, the study showed that when the width-to-thickness ratio of strips was properly used, the strength, stiffness, and energy absorption capabilities of slotted SPSWs were significantly increased, whereas the out-of-plane displacement was minimized by 40.00 %.

According to the technical literature, the amount of chloride that is transported by air from the sea surface depends on the amount of salt in the seawater in that area, the speed and direction of the wind, and the distance from the sea. Accordingly, data on the highest annual wind speed and direction of the wind are collected in several reinforced concrete structures (RC structures) in southern cities near the Persian Gulf at different distances from the sea. In this paper, by applying probabilistic modeling and utilizing the Hasofer–Lind and Rackwitz–Fiessler (HL-RF) method of reliability by aligning the enhanced Colliding Bodies Optimization method (ECBO) algorithm, and utilizing the data from the National Meteorological Organization, for concrete structures located in different distances with different speeds and directions of the wind from the Persian Gulf, the time of chloride corrosion initiation in reinforced concrete structures and the durability of these structures has been surveyed.

Tehran's water consumption (TWC) is rising as a result of rapid population growth, climate change, and precipitation decline. As water resources of Tehran are also affected by a variety of factors, the water supply scheme becomes so complicated and it is necessary to consider the complexity and dynamics interactions in water supply system before any decision making. In this study, Karaj reservoir as an important surface water resource of Tehran’s water supply system was modeled through system dynamics (SD) approach for prediction of Karaj Dam share in Tehran water supply. The SD model was implemented in AnyLogic software using the historical data from April 2006 to March 2022, and the stock and flows and dynamics variables were predicted for April 2023 to March 2023. The novelty of this research is the development of SD model of Karaj Dam to simulate its relationships and interactions for prediction of Karaj Dam share in Tehran water supply in 2023. In this regard, the TWC and Karaj Dam inflow were predicted by using SARIMA(1,0,0)(0,1,1)12 model for April 2023 to March 2023. Finally, to assess the precision of the results obtained from the SARIMA and SD models, the criteria of coefficient of determination (R2), Error percentage (E%), and Nash–Sutcliffe model efficiency coefficient (NS%) was calculated. The results showed that the Karaj Dam inflow will be decreased during April 2023 to March 2023 due to the precipitation decline, consequently the Karaj Dam reservoir volume will be reduced and for this reason less water can be harvested from Karaj Dam reservoir for different applications. Therefore, it is clear that in the future we will have faced the challenge of water supply in Tehran.

This paper investigates future changes in annual mean precipitation and air temperature across the Volga River basin, which serve as significant drivers of climate-induced changes in the Volga River's discharge, the primary input to the Caspian Sea. The thirteen Global Climate Models (GCMs) outputs under four Shared Socioeconomic Pathways (SSPs) scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) from the sixth phase of Coupled Model Intercomparison Project (CMIP6) are used for this study. In the historical period (1950-2014), using comprehensive rating metrics and Taylor diagram, the GCMs are ranked according to their ability to capture the temporal and spatial variability of precipitation and air temperature. The Multi-Model Ensemble (MME) is generated, and bias-correction techniques are utilized to reduce the uncertainties and correct the biases in CMIP6 outputs. Bias-correction techniques are assessed in the historical period and the average of proper methods utilized for future projections (2015-2100). In the 21st century, future projections show that the Volga River basin could mainly experience a temperature increase of 0.4°C to 7.5°C, alongside a precipitation rise of 0.7% to 37%, depending on the scenarios considered. Comparison of future projections with an observational dataset from 2015 to 2017 indicates that the SSP2–4.5 is more likely scenario to represent the future climate of the Volga River basin.

Driving speed has always played an important role in safety. Many variables influence the driver's behavior to choose the speed, one of which is the individual's psychological variables. In this study, the direct and indirect relationship of a series of psychological variables (latent variables) on the mean speed and the relationship of the latent variables with each other have been investigated. For this purpose, two questionnaires of aggressive drivers and DBQ were used. Also, a driving simulator was used to record the mean speed of the driver . The number of participants is 71 (38% women and 62% men). one proposed model that includes 8 hypotheses was modeled using the structural equation model (partial least square method) that hypotheses were significant at the 99% level. And the results showed that there is a direct relationship between attitude with hostile behavior (H1), hostile behavior with risky violation (H3) , hostile behavior with Self-willed violation (H4), risky violation with Self-willed violation (H5) and Self-willed violation with Inexperience violation/error (H6). subjective norm have inverse relationship with risky violation (H2). Also, people who have a high Self-willed violation usually drive at a high speed (H7).

Due to the increasing demand in construction and use of different types of piles on the one hand and the high cost of conducting large-scale tests on different types of piles, on the other hand, new methods have been proposed to study the behavior of different types of piles. Physical modeling provides the researcher the capability of studying model piles in the scaled environment at low costs. Among the different methods of physical modeling, the use of frustum confining vessels (FCV) has gained attraction in recent years. FCV is a cone-shaped vessel that can produce a stress distribution similar to the idealized linear stress distribution in depth. Helical piles are common types of deep foundations which were first used about 200 years ago. Helical Apiles are driven to the soil by applying a torque to the end of piles in the presence of vertical loads. Their quick and noise-free installation method, the minimal disturbance during the installation, and  environmental compatibility make them popular for working in urban areas. In this research, using the finite element method, the optimal dimensions of FCV apparatus were selected, and the FCV apparatus with optimal dimensions were constructed. A total of 18 compression tests were performed on Anzali sand in different relative densities and moisture contents, using single-helix and three-helices piles. Results indicate that increasing the number of helices and relative density of soil increases the pile and sand contact and causes higher bearing capacity for helical piles. Soil saturation, on the other hand, significantly reduces the ultimate strength.

Previous studies revealed that traditional methods of damage detection (e.g., visual inspection) are time-consuming and require large monetary resources. In the last three decades, machine learning algorithms, sensor technologies, and computer science have progressively advanced, which paved the way for implementing machine learning-based damage detection frameworks. This paper presents a damage detection framework for civil structures using machine learning algorithms. The decision-tree classifier is used to classify the state of damage in the building based on the damage indicators obtained from the output acceleration signals of the building. The braced-frame structure known as the IASC-ASCE structural health monitoring benchmark building was used to verify the presented approach. The total number of 6000 Gaussian white noise signals with 10s length was applied to the case study model as ambient vibrations using the Matlab platform. Five different damage indicators, including the vibration intensity, mean period, mean, variance, and the fundamental frequency of the structure, were used to train the classifier. A Bayesian Optimization algorithm was implemented to tune the hyperparameters of the decision-tree classification learner. The results indicate that the proposed approach could estimate the state of damage in the building with promising accuracy.

Numerical modeling of problems with large deformations is one of the main challenges in computational mechanics. Conventional numerical approaches cannot accurately model large deformations. Recently, the material point method (MPM), which comprises advantages of Eulerian and Lagrangian descriptions of movement, has been developed to solve complicated numerical problems such as large deformations. In this paper, the MPM method is employed to model the behavior of a soil mass behind a rigid retaining wall during active movement. It is the first time that the accuracy of the MPM method has been evaluated in the modeling of retaining walls with active movements. The accuracy and efficiency of the MPM are measured using two small-scale physical modeling tests and an analytical approach (for translational motion). In addition, a comparison between the results of the MPM and conventional FEM is provided. It is shown that the MPM can model the arching effect in the soil media better than the FEM; however, the material point method leads to smaller stresses on the wall compared to experimental results. It is demonstrated that the employed MPM can accurately model arching effects on the soil media behind the retaining walls with active movement. For transitional movement, arching effects lead to the upward movement of the resultant horizontal force on the wall, which occurs higher than 1/3H (H is the height of the wall). The achieved results indicate that the traditional methods can lead to overestimated designs without considering arching effects.