Journal of Numerical Methods in Civil Engineering
Volume:9 Issue: 2, Dec 2024

This paper presents an application of the Boundary Element Method (BEM) in engineering to simulate and numerically analyze the process of accumulation of chloride ions in a reinforced concrete structure. The study begins with a brief review of the origin of reinforced concrete and the phenomenon of depassivation of reinforcement. The geometric and mathematical model considers two types of concrete characteristics, seeking to numerically represent the concrete used in the construction of beams and pillars of buildings and bridges, covered with an external layer and/or surface protection. In the simulations, it was possible to record the concentration of chlorides in the position occupied by a steel rod in the reinforcement, calculating the number of years necessary to cause the steel to depassivate. In addition to concrete, two materials with different diffusivities were used for the coating layer and two values for its thickness, both related to the time required for the start of the reinforcement corrosion process. The results were obtained with a correlation level of 0.99954 for the R2 estimator, presented in the formulation validation section 4.1, allowing to obtain important information about the steel depassivation process with and without the use of surface protection, making it possible to calculate the start time of reinforcement depassivation under different conditions, the details of which are presented below.

The importance of the parameters of any optimization algorithm, especially meta-heuristic algorithms that have been created to simplify the solution of optimization problems, is inevitable. The optimal values of these parameters, which generally depend on the specifics of the problem in question, have a significant impact on the performance of the mentioned algorithms and a better search of the solution space. Parameters selection of them will play an important role in performance and efficiency of the algorithms. This article examines the capability of various optimization algorithms and suggests dual hybrid optimization algorithms are named PSO-FA, PSO-GA, PSO-GWO, for solving the problem of computing the depth and location of cracks in cantilever beams. The performance of Particle swarm optimization (PSO), Genetic algorithm (GA), Grey wolf optimization (GWO), Firefly algorithm (FA), and hybrid of them base on PSO optimizer to determine the location and depth of crack for cantilever beam are proposed. These suggested algorithms are optimization algorithms based on intelligent optimization. So, the performance of these algorithms are analyzed when the control parameters vary.

The smoothed particle hydrodynamics (SPH) and moving particle semi-implicit (MPS) are well-known and efficient mesh-less numerical methods widely used to investigate a wide range of complicated practical engineering problems. Recently, two modified Laplacian models [1, 2] have been proposed by using different efficient mathematical techniques, and the analogy between SPH and MPS methods. These two models exhibit significantly superior precision in comparison with several existing modified schemes [3-9] but still suffer from lower accuracy near calculation domain boundaries as they work with the conventional weight or interpolation functions. In this paper, the models were reformulated and further improved by replacing the weight functions with well-known moving least squares (MLS) shape functions without requiring dummy calculation nodes beyond boundaries. The proposed Laplacian models in this study could achieve very accurate results compared with the existing models [1, 2] for the solution of four different two-dimensional Poisson equations on irregular node distributions.

bearing capacity estimation of shallow foundations is the essential requirement in the design of structures and taking a calculation method into account is necessary. All the parameters and uncertainties cannot be factored in by the classic analytical-based methods. Moreover, performing in-site tests require an extensive period of time and many resources. With the development of new methods such as Machine Learning (ML) algorithms in recent decades, a resolution to these challenges has been identified. In this study, classic machine learning regression methods such as KNN, SVM and Decision Tree based models alongside the utilization of Artificial Neural Networks (ANN) regression are examined and compelling results are demonstrated. The dataset in this study is consisting of 97 tests on model foundations and site loadings on granular soil. The results indicate that ML regression methods will have reliable outcome in determination of bearing capacity. But more importantly, the precision of the trained model is closely correlated to data splitting and the ratio of train and test series in the dataset. The importance of splitting procedure was examined through trial and error with parameters of train test data ratio and the random state of sampling. It is indicated that a ratio of 80% for the training set would be an optimum value. Furthermore, relative importance of the input features was examined through a sensitivity analysis which indicated that the internal friction angle of the soil and the depth of the foundation are the most important inputs while using ML regression methods.

With the increasing speed and weight of modern passenger aircraft, the need for longer runways has become more critical than ever. To address safety concerns, the Federal Aviation Administration (FAA) has mandated a 305-meter (1,000-foot) safety zone, known as the Runway Safety Area (RSA), at the end of runways at major airports. However, in many cases, this requirement cannot be met due to natural or man-made obstacles within the airport's boundaries. As a solution, the implementation of an Engineered Material Arrestor System (EMAS) has been proposed. EMAS is designed to significantly reduce the stopping distance of aircraft during overrun events, minimizing both passenger discomfort and the risk of structural damage to the aircraft. The objective of this paper is to investigate and simulate the performance of EMAS using finite element analysis software capable of handling large deformation problems. The Arbitrary Lagrangian-Eulerian (ALE) formulation is utilized to conduct large deformation analyses. In the simulations, three types of aircraft are modeled to enter a hypothetical EMAS bed at a speed of 70 knots (130 km/h). Additionally, three types of foam concrete with different densities are selected for the EMAS bed material. The results demonstrate that higher-density materials exhibit greater stiffness, resulting in shorter stopping distances for the aircraft. As expected, lower-density (softer) materials apply less force and deceleration to the aircraft. Furthermore, the findings indicate that lighter aircraft experience higher deceleration forces than heavier aircraft, regardless of the bed material. However, heavier aircraft generate higher overall impact forces during the overrun.

Earth observation satellites possess key characteristics such as high temporal and spatial resolution, wide coverage imaging, multispectral capabilities, and access to a rich archive of images through various gateways. This enables the regular and continuous monitoring of phenomena related to various pollutants on a global scale. Presently, weather models leverage satellite imagery to record and report pollutant levels hourly and daily, allowing for the identification of sources and patterns of pollutants. Remote sensing technology, with its ability to measure concentration and intensity, as well as track the movement and changes in pollutant locations, has multiple applications in assessing air quality. The use of this technology facilitates informed decision-making and expert analysis to reduce air pollution, contributing to the goal of achieving clean and healthy air on the planet. In this report, Sentinel 5 satellite data was utilized to monitor NO2 gas emissions in the city of Ankara. The data was analyzed month by month over the course of a year, providing valuable insights into the trends and patterns of NO2 concentrations.at the end of this article we will find that Google Earth Engine and remote sensing may replace ground stations, shifting their role to validation. The study finds high similarity in ground and satellite data, especially in analyzing NO2 concentration and its seasonality. Utilizing advanced satellite tech, like Google Earth Engine, emphasizes language and technology in addressing environmental challenges for a sustainable future

Steel Shear Walls (SSWs) exhibit suitable stiffness among various lateral force-resisting systems, and their application has been extended to tall buildings. In this research, the optimization of a High Performance-Based Seismic Design (HPBSD) of SSWs was introduced using a new optimization method. A hybrid algorithm was developed based on the Harmony Search (HS) algorithm, Multi-Design Variable Configuration (Multi-DVC) cascade optimization, and Upper-Bound Strategy (UBS). This new approach, termed MDVC-UHS, utilized cascade structural sizing optimization to manage numerous variables through a series of DVCs. The UBS was employed to reduce computational time, while the HS was used for global optimization. The MDVC-UHS algorithm was applied to optimize the dimensions of the steel shear wall in accordance with high performance-based seismic design principles. The research indicated that as the length of the SSW increased relative to its height, the more it was in the shear mode and the more usage it could have.

In the present study, the temporal variation of Sentinel-5P TROPOMI-derived air pollutants (NO2 and SO2,) and MODIS-derived AOD were examined by using satellite missions in six air pollution’ hotspots provinces, including Isfahan, Tabriz, Mashhad, Tehran, Ahvaz, and Guilan in Iran for contemporaneous time periods before (as a baseline period), and during the epidemic, including the first wave lockdown period of the COVID-19 outbreak, from the 22nd of February, 2019, to the 22nd of February, 2021. The results revealed that the mean ratio of NO2 and SO2 has not varied drastically in the considered provinces. These column concentration ratios for all months were within the range of +2%(Tehran) to -6% (Tabriz). In comparison, the ratio of variance is more considerable, especially for Guilan province, a tourist attraction province, even in travel restrictions and lockdowns in Iran. The AOD distribution map and its trend illustrated that Guilan, Khorasan-Razavi, and Tabriz had become more pollutants after the outbreak due to changes in tourist patterns and emission inventories. Base on wavelet transform, implemented on ground-measurment of PM2.5, the PM2.5 concentration increased in early 2021 from the value in baseline period, due to eased restriction and expansion of public COVID-19 vaccine in all considered provinces.