Artificial intelligence for prediction of physical and mechanical properties of stabilized soil for affordable housing

Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Soil stabilization is the alteration of physicomechanical properties of soils to meet specific engineering requirements of problematic soils. Laboratory examination of soils is well recognized as appropriate for examining the engineering properties of stabilized soils; however, they are labor-intensive, time-consuming, and expensive. In this work, four artificial intelligence based models (OMC-EM, MDD-EM, UCS-EM+, and UCS-EM−) to predict the optimum moisture content (OMC), maximum dry density (MDD), and unconfined compressive strength (UCS) are developed. Experimental data covering a wide range of stabilized soils were collected from previously published works. The OMC-EM, MDD-EM, and UCS-EM− models employed seven features that describe the proportion and types of stabilized soils, Atterberg limits, and classification groups of soils. The UCS-EM+ model, besides the seven features, employs two more features describing the compaction properties (OMC and MDD). An optimizable ensemble method is used to fit the data. The model evaluation confirms that the developed three models (OMC-EM, MDD-EM, and UCS-EM+) perform reasonably well. The weak performance of UCS-EM− model validates that the features OMC and MDD have substantial significance in predicting the UCS. The performance comparison of all the developed ensemble models with the artificial neural network ones confirmed the prediction superiority of the ensemble models.Peer reviewe

Embodied energy and CO2 emissions of widely used building materials : The Ethiopian context

Buildings use a wide range of construction materials, and the manufacturing of each material consumes energy and emits CO2. Several studies have already been conducted to evaluate the embodied energy and the related CO2 emissions of building materials, which are mainly based on case studies from developed countries. There is a considerable gap in cases of developing countries regarding assessment of embodied energy and CO2 emissions of these building materials. This study identified the top five most used construction materials (cement, sand, coarse aggregates, hollow concrete blocks, and reinforcement bars), which are also prime sources of waste generation during construction in the Ethiopian building construction sector. Then, what followed was the evaluation of the embodied energies and CO2 emissions of these materials by examining five commercial and public buildings within the cradle-to-site lifecycle boundary. The evaluation results demonstrated that cement, hollow concrete blocks (HCB), and reinforcement bars (rebars) are the major consumers of energy and major CO2 emitters. Cumulatively, they were responsible for 94% of the embodied energy and 98% of the CO2 emissions. The waste part of the construction materials has inflated the embodied energy and the subsequent CO2 emissions considerably. The study also recommended several strategies for the reduction of embodied energy and the related CO2 emissions. The research delivers critical insights into embodied energy and CO2 emissions of the five most used building materials in the Ethiopian construction industry, as there are no prior studies on this theme. This might be a cause to arouse awareness and interest among the policy makers and the wider public to clearly understand the importance of research on this crucial issue to develop national energy and CO2 descriptors for construction materials, in order to take care of our naturally endowed, but yet fragile, human habitat.Peer reviewe

Prediction of Compaction and Strength Properties of Amended Soil Using Machine Learning

In the current work, a systematic approach is exercised to monitor amended soil reliability for a housing development program to holistically understand the targeted material mixture and the building input derived, focusing on the three governing parameters: (i) optimum moisture content (OMC), (ii) maximum dry density (MDD), and (iii) unconfined compressive strength (UCS). It is in essence the selection of machine learning algorithms that could optimally show the true relation of these factors in the best possible way. Thus, among the machine learning approaches, the optimizable ensemble and artificial neural networks were focused on. The data sources were those compiled from wide-ranging literature sources distributed over the five continents and twelve countries of origin. After a rigorous manipulation, synthesis, and results analyses, it was found that the selected algorithms performed well to better approximate OMC and UCS, whereas that of the MDD result falls short of the established threshold of the setlimits referring to the MSE statistical performance evaluation metrics.Peer reviewe

Experimental Investigation on the Utilization of Marble and Scoria Powder as Partial Replacement of Cement in Concrete Production

This paper explores how marble and scoria powder can be used as partial substitutes for ordinary Portland cement in creating C-25 concrete. Both materials contain over 50% of the major oxides found in cement, with marble high in CaO and scoria high in SiO2. Experimental investigations were conducted to study the chemical, physical, mechanical, and fresh properties of concrete containing marble and scoria powder. For the investigation, 13 different mixes, including the control mix, were used with a constant water–cement ratio of 0.5 and a slump range of 25–50 mm for concrete with a compressive strength (CS) of 25 MPa. Marble-to-scoria ratio of 2 : 1, 1 : 1, and 1 : 2 was used, and then the combined fraction of both marble waste and scoria in concrete was increased from 0% to 20% in 5% range. Including the control test specimens, a total of 117 (150 × 150 × 150 mm) concrete cubes for CS test, 39 (100 × 100 × 500 mm) concrete beam specimens for flexural strength test, 39 (100 × 200 mm) cylinder specimens for splitting tensile strength (STS) test and, 39 (100 × 100 × 100 mm) cube specimens for water absorption test were cast and tested at 3, 7, 28, and 56 days. The test results indicate that marble and volcanic scoria powders with marble-to-scoria ratio of 1 : 1 could replace cement up to 15% without compromising the CS and up to 10% without compromising the flexural and STS; also, the water absorption decreases up to 10% replacement; however, the workability of the fresh mix decreases as the combined replacement level of marble and scoria increases. Generally, a 10% replacement with marble-to-scoria ratio of 1 : 1 produces concrete with higher compressive, flexural, tensile strength, and water absorption manifestations when compared to conventional concrete