Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network

Stabilizing sand with cement is considered to be one of the most cost-effective and useful methods of in-situ soil improvement, and the effectiveness is often assessed using unconfined compressive tests. In certain cases, zeolite and cement blends have been used; however, even though this is a fundamental issue that affects the settlement response of a soil, very few attempts have been made to assess the stress-strain behaviour of the improved soil. Also, the majority of previous studies that predicted the unconfined compressive strength (UCS) of zeolite cemented sand did not examine the effect of the soil improvement variables and strain concurrently. Therefore, in this paper, an initiative is taken to predict the relationships for the stress-strain behaviour of cemented and zeolite-cemented sand. The analysis is based on using the unconfined compression test results and Group Method of Data Handling (GMDH) type Neural Network (NN). To achieve this end, 216 stress-strain diagrams resulting from unconfined compression tests for different cement and zeolite contents, relative densities, and curing times are collected and modelled via GMDH type NN. In order to increase the accuracy of the predictions, the parameters associated with successive stress and strain increments are considered. The results show that the suggested two and three hidden layer models appropriately characterise the stress-strain variations to produce accurate results. Moreover, the UCS values derived from this method are much more accurate than those provided in previous approaches. Moreover, the UCS values derived from this method are much more accurate than those provided in previous approaches which simply proposed the UCS values based on the content of the chemical binders, compaction, and/or curing time, not considering the relationship between stress and strain. Finally, GMDH models can be considered to be a powerful method to determine the mechanical properties of a soil including the stre

Optimized Cement-Zeolite Mixtures for Sustainable Sand Improvement: Predictive Model Code

<p>The repository is a comprehensive resource for researchers and practitioners in the field of civil engineering and environmental sustainability. It contains a Python code developed to process and analyze data derived from an extensive experimental study on optimizing cement-zeolite mixtures for sand improvement.</p><p>The code utilizes advanced machine learning techniques, including a back-propagation neural network (BPNN) for predicting mechanical strength and an adaptive geometry estimation-based multi-objective evolutionary algorithm (AGE-MOEA) for optimization. The repository is linked to a detailed dataset available in the Harvard Dataverse, providing the input and output parameters crucial for understanding the unconfined compressive strength of the treated sand samples.</p><p>To ensure transparency and foster scientific collaboration, the code is released under the CC BY 4.0 license, encouraging sharing, modification, and use of the dataset, provided appropriate credit is given. This repository not only aids in replicating the study's findings but also serves as a foundation for future research endeavors aiming to enhance the sustainability of construction materials.</p><p>In the shared code, preprocessing steps are meticulously laid out, including data normalization and splitting into training and testing sets to ensure robustness and reliability of the predictive models.</p><p>Researchers who wish to delve deeper into the nuances of the data and its analysis will find the code an invaluable tool for navigating and expanding upon the frontiers of sustainable civil engineering solutions.</p&gt

The effect of cement and zeolite on undrained shear strength of the expansive clay

Expansive soils can be problematic in that they impose significant economic damages on the construction projects worldwide due to their high volume change in wet-dry conditions. Annually, many structures have been constructed on expansive soils such that nearly 60 percent of them undergo minor damage such as different kinds of fissure while 10 percent face severe unrehabilitatable damages. In this research, the cement and zeolite have been used as the base and supplementary cementitious stabilizers, respectively, and the undrained behavior of the treated expansive clay has been investigated. Hence, 6, 8, 10, and 12 percent of cement and 10, 30, 50, 70, and 90 percent of zeolite replacement were employed to cast the specimens. In the following, using the unconfined compression strength (UCS) and unconsolidated undrained triaxial (UU) tests, it can be stated that the best geotechnical performance such as maximum UCS, deviator stress in UU tests, secant modulus of elasticity (E50), cohesion, and internal fraction were obtained at 12% cement addition and 30% zeolite replacement after 28 days of curing. In both tests, increase in the cement content led to the increment in the failure strength of the samples. Based on the analysis of the results, it can be stated that the best geotechnical characteristics such as UCS, maximum deviational stress in triaxial test, secant modulus of elasticity (E50), and cohesion and internal friction angle were derived from the specimen stabilized with 12 percent of cement and 30 percent zeolite replacement cured in 28 days. With the above said, it can be noted that the optimum amount of zeolite replacement was 30%. Cement increment led to the enhancement of mechanical performance. Increase in zeolite replacement reduced the brittleness of the samples. In addition, increase in the curing time improved the mechanical behavior of the stabilized samples. The microscopic view justified the improvement of the stabilized samples

Prediction of compressive and tensile strengths of zeolite-cemented sand using porosity and composition

The current paper aims to examine the effects of cement and zeolite contents on the strength of zeolite-cemented sand using the unconfined compressive strength (UCS) and splitting tensile strength (q(t)). First of all, the optimum content (i.e., the corresponding water content to the maximum UCS) was obtained from the response surface (RSM) and central composite design (CCD) methods. Then, unconfined compression and splitting tensile tests considering four distinct porosity percentages (TO related to D-rsand = 35, 50, 70 and 85% (Dr = relative density), five cement contents (2, 4, 6, 8. and 10%), and six different percentages of zeolite replacement (0, 10, 30, 50, 70 and 90%) were performed. Then, the amounts of the improved UCS and qt of the specimens as a result of the porosity, zeolite and cement were measured. The results indicated that the 30% replacement of cement with zeolite (Z) was found the optimum amount of replacement. The strength improvement rate of the optimum zeolite-cemented sand (Z = 30%) compared to the mere cemented sand (Z = 0%) increased with the increase in the cement content as well as increase in the porosity of the compacted mixture. Based on the results of the zeolite-cement-sand mixtures, it has been shown that the UCS and qt improved by increasing the cement content (C). Also, the power function is well-matched to fit (UCS and qt)-C. The active composition parameter (AC) participate in the chemical reaction was introduced, as the minimum amount of either CaO or Al2O3 + SiO2. Afterward, the UCS and qt were plotted against the porosity/active composition parameter (VAC), which is regarded as a controlling and key parameter of the UCS and qv Also, the experimental results and the parameter of eta(-1.79)AC(1.43) introduce an acceptable description of the mechanical strength. Finally, the q(t)/UCS relationship is unique for the zeolite-cemented sand studied, being independent of the eta/AC. (C) 2019 Elsevier Ltd. All rights reserved

Detection of Early Stage Apoptotic Cells Based on Label-Free Cytochrome c Assay Using Bioconjugated Metal Nanoclusters as Fluorescent Probes

Cytochrome <i>c</i> (Cyt <i>c</i>) is an important biomarker in cell lysates for the early stage of apoptosis or anticancer agents. Here, two novel label-free fluorescence assays based on hemoglobin-stabilized gold nanoclusters (Hb/AuNCs) and aptamer-stabilized silver nanoclusters (DNA/AgNCs) for analysis of Cyt <i>c</i> are presented. The heme group of the protein induces sensitive sensing platforms accompanied by the decreased fluorescence of both metal nanoclusters. The quenching processes observed found to be based on the fluorescence resonance energy transfer mechanism from Hb/AuNCs to Cyt <i>c</i> and photoinduced electron transfer from DNA/AgNCs to the aptamer-Cyt <i>c</i> complex. The linear range for Cyt <i>c</i> was found to be 0–10 μM for Hb/AuNCs and from 0 to 1 μM for DNA/AgNCs, with limits of detection of ∼15 nM. On the basis of strong binding affinity of DNA aptamers for their target proteins, the DNA/AgNCs probe was successfully applied to the quantitative determination of Cyt <i>c</i> in cell lysates, which opens a new avenue to early diagnostics and drug screening with high sensitivity. Compared to the conventional Western blot method, the presented assays are low cost, easy to prepare the fluorescent probes, and sensitive, while overall time for the detection and quantitation of Cyt <i>c</i> from isolated mitochondria is only 20 min. The proposed method for Cyt <i>c</i> detection may also be useful for the study of those materials that cause mitochondrial dysfunction and apoptotic cell death