Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
The expansive soil swells significantly in the presence of moisture, which often leads to the failure of superstructures. Conventional stabilization techniques are applied in many instances, although environmental issues are of significant concern for such stabilization. Keeping this in mind, an attempt is made to apply a new approach for stabilizing different types of expansive soils, treated with a nonconventional binder geopolymer that utilizes fly ash as the main ingredient. A series of laboratory experiments are run to determine the engineering properties of treated soils with varying percentages of geopolymer from 0% to 30%. The experimental investigation involved tests such as unconfined compressive strength, compaction, Atterberg limits, and swelling pressure. Significant strength development occurs with increasing percentages of geopolymer, and their swelling pressures decrease considerably. Additionally, a series of California Bearing Ratio (CBR) tests were undertaken to assess the suitability for road construction. The optimum dosage of the stabilizing agent is found to be 20%, as justified by studies in the literature. Furthermore, scanning electronic microscope (SEM) images of the treated samples revealed microstructural changes in the soil matrix, which strongly correlate with the improvement of strength and swelling behavior. Hence, based on our experimental results, 20% geopolymer content is sufficient for enhancing the engineering properties of expansive soils, and the treated soils can directly be used as subgrade or sub-base material....
Compressive strength of concrete is an important parameter in the design of concrete structures and the prediction of their durability. Therefore, it is of great significance to predict the compressive strength of concrete. In this study, a fully connected neural network model is developed using the PyTorch framework to predict the compressive strength of concrete and compared with six other machine learning models. These models are multiple linear regression, K-nearest neighbor regression, support vector machine, decision tree, random forest, light gradient boosting machine, and artificial neural network. The model is trained using 4,253 data with seven input parameters, including cement (C), fly ash (F), mineral powder (K), fine aggregate (FA), coarse aggregate (CA), water reducer admixture (WRA), and water (W). Three thousand six hundred twenty-one data in the datasets are used to train the prediction model after data cleaning, and 632 data are used to validate the model. The results show that the fully connected neural network model based on PyTorch frame can predict the compressive strength of concrete with higher accuracy. Therefore, it is a reliable and useful method to optimize the artificial network model. So, it has important application value in practice. The strength of concrete can be predicted in advance, making the project more efficient and reducing costs. Besides, by adjusting the mix ratio, combining the strength prediction results in different environments and industries to ensure the quality of construction....
Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders....
Assessing the most important cost-influencing factors is essential for enhancing the predictive ability of cost estimation for building construction projects. The goal of this study is to examine and design a valid cost prediction model for assessing factors that impact the cost estimation of public buildings in Addis Ababa. This research solves these issues that typically arise in predictive cost estimation models in two major processes. First, the insights of 133 professionals gathered on the 38 cost-impacting elements, and 15 top factors design, time or cost, and parties’ experience were determined. The suggested hybrid approach is based on the Akaike information criterion (AIC) and principal component regression (PCR) employed, coupling a stepwise linear regression model. According to the findings of the study, principal component analysis reduced important factors to 14 and efficiently solved the problem of multicollinearity with a variance inflation factor of less than 2, while stepwise cross-validation solved the overfitting problem at the lowest AIC. The cost prediction model sorted out five factors: design completion by the public body when bids are invited; completion of the project scope definition when bids are invited; level of construction complexity; importance of project completion within budget; and subcontractor experience and capability have all been identified as the main cost-determining factors. The study’s contribution is the first approach (PCR–AIC) utilized in this work to explore numerous cost-estimating components, eliminate those that were related to one another, and identify the most crucial ones that consisted of the majority of the original variables’ attributes....
Concrete prepared using Gobi sand and gravel instead of ordinary sand and gravel is referred to as Gobi concrete. In order to explore the effect of fibers on the frost resistance of Gobi concrete, as well as to enhance the service life of Gobi aggregate concrete in Northwest China, experiments were conducted with fiber types (polypropylene fibers, basalt fibers, polypropylene–basalt fibers) and fiber volume fractions (0%, 0.1%, 0.2%, 0.3%) as variable parameters. This study investigated the surface morphology, mass loss rate, and relative dynamic elastic modulus of fiberreinforced Gobi concrete after different freeze–thaw cycles (0, 25, 50, 75, 100). Corresponding frost damage deterioration models were proposed. The results indicate that fibers have a favorable effect on the anti-peeling performance, mass loss rate, and dynamic elastic modulus of Gobi aggregate concrete. The improvement levels of different fiber types are in the following order: 0.1% basaltpolypropylene fibers, 0.2% polypropylene fibers, and 0.3% basalt fibers. Compared to Gobi concrete exposed to natural environmental conditions, the freeze–thaw cycle numbers increased by 343, 79, and 69 times, respectively. A quadratic polynomial damage model for fiber-reinforced Gobi concrete, using relative dynamic elastic modulus as the damage variable, was established and demonstrated good predictive performance....
Loading....