Effect of Building Rotation on Thermal Energy Reduction and Total Solar Gain in Tehran Residential Buildings

Decreasing energy consumption is the one of the most important subject. Besides, in any building design, one employs simple techniques such as orientation, shading of windows, color, and vegetation among others, to create comfortable conditions. Accordingly, Total thermal and solar gain and the effective factors on them should be investigated and optimized. The objective of this research was to find out the impact of rotation on total thermal and solar gain which can lead us to achieve less energy-use in Iran. The software used for this research was Grasshopper and Lady Bug and Honey bee plugins. The typical plan without any environment effects was simulated and effect of rotation on total thermal and solar gain was analyzed. The results showed that using south radiation can be helped to decline total thermal and energy consumption. However, solar gain for east and west radiation was in maximum level. Keywords: thermal energy, rotation, energy reduction, building simulation, residential buildin

Influence of natural fillers on shear strength of cement treated peat

U radu su prikazana istraživanja stabilizacije tresetnog tla cementom i različitim prirodnim punilima. U svrhu istraživanja, prirodna punila u različitim su omjerima pomiješana s tresetom ojačanim cementom te su izmjerene tlačne čvrstoće. Rezultati upućuju na to da mješavina s 300 kg/m3 cementa i 125 kg/m3 dobro granuliranog pijeska u odnosu na masu vlažnog treseta ima najveću jednoosnu tlačnu čvrstoću nakon njege od 90 dana. Ostala su punila smanjila čvrstoću stabiliziranog treseta.A study on peat soil stabilization by using cement and different natural fillers is presented in the paper. In the scope of this study, natural fillers are mixed at various dosages with cement treated peat in order to evaluate the unconfined compressive strength. The results indicate that the mix design of 300 kg/m3 cement, with 125 kg/m3 of well graded sand by mass of wet peat, gives the highest uniaxial compressive strength at 90 days of curing. Other fillers decrease the strength of stabilized peat

Diagnostic accuracy of circular RNA for diabetes Mellitus : a systematic review and diagnostic Meta-analysis

Acknowledgements: We thank all our staff at the Tehran University of medical sciences and at Kurdistan University of Medical sciences who helped us in this work. We also acknowledge the papers that we used and participants in those papers. Funding Information: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Bearing capacity prediction of shallow foundation on sandy soils:a comparative study of analytical, FEM, and machine learning approaches

In this study, we compared the Ultimate Bearing Capacity (UBC) of shallow foundations on sandy soils that were predicted using Analytical, Finite Element Modeling (FEM), and Machine Learning (ML) approaches for predicting the Ultimate Bearing Capacity (UBC) of shallow foundations on sandy soils. For the first of its type, we presented a novel Python-based pipeline that enables rapid and precise estimation of the UBC for shallow foundations, surpassing traditional methods by providing superior speed and accuracy. The proposed models consider the foundations’ geometry and soil properties as input parameters. We created, trained, and tested nineteen ML models using the Pycaret library in the Google Colab environment. Furthermore, we conducted a comparative analysis of twelve new datasets derived from the training process. Our objective was to estimate the UBC values using three established techniques: (a) the widely recognized Terzaghi method, (b) the advanced three-dimensional FEM software (using OptumG3 software), and the ML-based method. Based on the ML results, we found that Gradient Boosting Regression (gbr), AdaBoost Regression (ada), Random Forest Regression (rf), and Extra Tree Regression (et) were the most effective models for estimating UBC. The gbr model exhibited the highest UBC prediction performance, attaining an R2 value of 1 on the training set, an R2 value of 0.937 on the test set, and an RMSE of 1.171 kPa. Using sensitivity analysis results, we demonstrated that the internal friction angle of the soil is the most significant input variable for estimating UBC, closely followed by the depth of the footing. The comparative results revealed that the well-known Terzaghi method and FEM modeling underestimate the UBC. The proposed user-friendly pipeline would be a valuable tool for geotechnical engineers to predict UBC values, providing a larger dataset in future research that can be trained and tested for the model to enhance reliability further

FEM-based modelling of stabilized fibrous peat by end-bearing cement deep mixing columns

This study aims to simulate the stabilization process of fibrous peat samples using end-bearing Cement Deep Mixing (CDM) columns by three area improvement ratios of 13.1% (TS-2), 19.6% (TS-3) and 26.2% (TS-3). It also focuses on the determination of approximate stress distribution between CDM columns and untreated fibrous peat soil. First, fibrous peat samples were mechanically stabilized using CDM columns of different area improvement ratio. Further, the ultimate bearing capacity of a rectangular foundation rested on the stabilized peat was calculated in stress-controlled condition. Then, this process was simulated via a FEM-based model using Plaxis 3-D foundation and the numerical modelling results were compared with experimental findings. In the numerical modelling stage, the behaviour of fibrous peat was simulated based on hardening soil (HS) model and Mohr-Coulomb (MC) model, while embedded pile element was utilized for CDM columns. The results indicated that in case of untreated peat HS model could predict the behaviour of fibrous peat better than MC model. The comparison between experimental and numerical investigations showed that the stress distribution between soil (S) and CDM columns (C) were 81%C-19%S (TS-2), 83%C-17%S (TS-3) and 89%C-11%S (TS-4), respectively. This implies that when the area improvement ratio is increased, the share of the CDM columns from final load was increased. Finally, the calculated bearing capacity factors were compared with results on the account of empirical design methods

Ultimate bearing capacity of strip footing resting on clay soil mixed with tire-derived aggregates

This study investigated the use of recycled tire-derived aggregate (TDA) mixed with kaolin as a method of increasing the ultimate bearing capacity (UBC) of a strip footing. Thirteen 1g physical modeling tests were prepared in a rigid box of 0.6 m × 0.9 m in plan and 0.6 m in height. During sample preparation, 0%, 20%, 40%, or 60% (by weight) of powdery, shredded, small-sized granular (G 1–4 mm) or large-sized granular (G 5–8 mm) TDA was mixed with the kaolin. A strip footing was then placed on the stabilized kaolin and was caused to fail under stress-controlled conditions to determine the UBC. A rigorous 3D finite element analysis was developed in Optum G-3 to determine the UBC values based on the experimental test results. The experimental results showed that, except for the 20% powdery TDA, the TDA showed an increase in the UBC of the strip footing. When kaolin mixed with 20% G (5–8 mm), the UBC showed a threefold increase over that for the unreinforced case. The test with 20% G (1–4 mm) recorded the highest subgrade modulus. It was observed that the UBC calculated using finite element modeling overestimated the experimental UBC by an average of 9%

A study on UCS of stabilized peat with natural filler: A computational estimation approach

This study applied two feed-forward type computational methods to estimate the Unconfined Compression Strength (UCS) of stabilized peat soil with natural filler and cement. For this purpose, experimental data was obtained via testing of 271 samples at different natural filler and cement mixture dosages. The input parameters for the developed UCS (output) model were: 1) binder dosage, 2) coefficient of compressibility, 3) filler dosage, and 4) curing time. The model estimated the UCS through two types of feed-forward Artificial Neural Network (ANN) models that were trained with Particle Swarm Optimization (ANN-PSO) and Back Propagation (ANN-BP) learning algorithms. As a means to validate the precision of the model two performance indices i.e., coefficient of correlation (R 2 ) and Mean Square Error (MSE) were examined. Sensitivity analyses was also performed to investigate the influence of each input parameters and their contribution on estimating the output. Overall, the results showed that MSE (PSO) R 2 (BP) ; suggesting that the ANN-PSO model better estimates the UCS compared to ANN-BP. In addition, on the account of sensitivity analysis, it is found that the binder and filler content were the two most influential factors whilst curing period was the least effective factor in predicting UCS