Assessing the Effect of Density and Water–Cement Ratio on the Cement Utilisation, Fluidity, Mechanical Properties and Water Absorption of Foam Concrete | Chapter 8 | Engineering Research: Perspectives on Recent Advances Vol. 7

 

Foam Concrete, also known as bubble concrete, is a lightweight building material with a cellular structure and is usually composed of cement mortar and foam. For FC, the determination of density and the water–cement ratio not only indirectly determines the content of its components, but it also plays an important role in influencing the workability and mechanical properties of FC. This study analyse the influence of density and the water–cement (W/C) ratio on the slurry fluidity, compressive strength, and water absorption of foamed concrete (FC) and its mechanism of action, with the aim of proposing an optimal mix ratio for FC to reduce cement usage and carbon emissions in the construction industry and ensure the good overall performance of FC. In this experiment, two groups of experiments were designed using the control variable method. The compressive strength of the FC block was tested using a uniaxial compression test, and the UPV and rebound value of the FC block were tested through nondestructive testing. The water absorption rate of the FC test block was tested. Fluidity and uniaxial compression tests showed that when the density was 600 kg/m3 and the W/C ratio was 0.6, the FC slurry had maximum fluidity, but its mechanical properties were poor, and it collapsed easily. Conversely, by analysing the uniaxial compressive strength/cement (UCS/C) ratio, it was observed that the mix ratio had a maximum cement utilisation rate (W/C ratio) of 0.5 and a density of 1000 kg/m3. The final analysis showed that the optimal mix ratio of FC in this test was W/C = 0.5, with a density of 1000 kg/m3. The results of this experiment can be used as a reference for basic experimental research in the field of foamed concrete. Further experiments are needed to investigate the effects of density and W/C ratio on other types of concrete, as well as to determine whether the working performance of foam concrete will change in actual construction environments.

 

Author(s) Details

Alipujiang Jierula
College of Architecture and Engineering, Xinjiang University, Urumqi 830046, China and Xinjiang Key Laboratory of Building Structure and Earthquake Resistance, Xinjiang University, Urumqi 830046, China.

 

Haodong Li
College of Architecture and Engineering, Xinjiang University, Urumqi 830046, China.

 

Yang Chen
College of Architecture and Engineering, Xinjiang University, Urumqi 830046, China.

 

Cong Wu
College of Architecture and Engineering, Xinjiang University, Urumqi 830046, China and Xinjiang Key Laboratory of Building Structure and Earthquake Resistance, Xinjiang University, Urumqi 830046, China.

 

Xiao Wu
College of Architecture and Engineering, Xinjiang University, Urumqi 830046, China.

 

Hanlin Yin
College of Architecture and Engineering, Xinjiang University, Urumqi 830046, China.

 

Please see the book here:- https://doi.org/10.9734/bpi/erpra/v7/5591

Predicting Wheat Production in Karnataka: A Linear Regression Approach | Chapter 28 | Innovative Solutions: A Systematic Approach towards Sustainable Future

Wheat is one of the High Yielding Varieties of agricultural crop. Uttar Pradesh, Madhya Pradesh and Punjab are the three largest wheat producing states in India whereas Belagavi, Vijayapura and Dharwad are three major wheat growing district in Karnataka. The secondary data on area, Production, yield along with season and Variety was collected for the year 2021-22 from Directorate of Economics and Statistics, Bangalore (https://des.karnataka.gov.in/). Here Linear Regression analysis has been made to know the impact of area on production of Wheat in different district of Karnataka. Trend analysis was performed by fitting linear model.

 

Author (s) Details

 

Ashwini H P
Department of Mathematics, Acharya Institute of Technology, Bengaluru, India.

 

Deepasree S Kumar
Department of Mathematics, Acharya Institute of Technology, Bengaluru, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-93-49238-47-3/CH28

An Ensemble of ML Algorithms for Predicting Accurate House Price | Chapter 9 | Science and Technology - Recent Updates and Future Prospects Vol. 3

 

In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market. This study aims to propose a system for “House price prediction” by using Machine Learning. This house price prediction is an approach that can precisely estimate the price of a new house based on its attributes using previous data on house features (such as square footage, number of bedrooms and bathrooms, location, etc.) and their corresponding prices.

To analyze the key elements and the best predictive models for home prices, literature research was conducted. Five algorithms namely linear regression, support vector machine, Lasso regression, Random Forest and XGBoost have been applied in this study to predict house prices using a dataset of real estate properties. Exploratory data analysis (EDA) was conducted for the house price prediction project. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Results also showed that Random Forest and XGBoost can handle high-dimensional datasets, capture complex relationships, and effectively manage feature interactions by their superior performance. This study's results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize for this research would both be greatly aided by this study, especially for housing developers and researchers.

 

Author(s)details:-

 

M. Jagan Chowhaan
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.

 

D. Nitish
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.

 

G. Akash
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.

 

Nelli Sreevidya
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.

 

Subhani Shaik
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.

 

Please See the book here :-  https://doi.org/10.9734/bpi/strufp/v3/202

Analysis of Sales Pattern Predictions on New Segment Norms Using Linear Regression | Chapter 7 | Research and Applications Towards Mathematics and Computer Science Vol. 8

This work aims to create a system that identifies the different factors and predicts the most accurate sales for a particular region with the help of history in that particular history. This is to resolve all the challenges and problems that occur while making orders daily. Building this tool will increase the overall sales of the country by aiming at the potential areas of growth which will directly lead to an increase in sales. The Revenue Growth Management analysis generates a sales prediction basedonvarious factors which will help in targeting manufacturing and so on. The aim is to generate a system that predicts accurate sales so least of the products are being manufactured with no use. This will help in targeting the niche market of buyers who are potential buyers.

Author(s) Details:

Logeswari Saranya R.,
Department of AI&DS, RMK Engineering College, Thiruvallur-601206, India.

Please see the link here: https://stm.bookpi.org/RATMCS-V8/article/view/13120

Simple Optimal Algorithm for Solving Rank-r Complex Equations Ax = b Rendering Sparse Solution xS with Support Surprisingly Confined to the Indices of r Independent Columns of A | Chapter 10 | Research and Applications Towards Mathematics and Computer Science Vol. 4

 In this item we will study optimal sparse answers for linear equatings  Ax = B, where A ∈ ℂ m×n is known, x ∈ ℂn is obscure, B ∈ ℂm is the measurement heading (possibly noisy, that is, b = Ax + e), and rank (A) = r . The proposed invention computes an optimal sparse answer, say xs = (xs(1) ,…, xs(n))T (T designates transposition), by using A to precompute indications α = (α1 ,…, αr) satisfying 1  ≤ α1 ≺ ⋯ ≺ αr ≤ n that show xs (β) = 0n-r , where β denotes α 's completing indices. We will show that xs(α) is a fundamental solution associated with the lines indexed by α of few intermediate underdetermined linear equatings whose underlying mold is (r x n)-dimensional. Hence, the cardinality s of s  ∶= support (x8) ∶= {k : xs (k)≠  0} satisfies s = r for a non-degenerate fundamental solution and s < r for a degenerate basic resolution. The proposed treasure is based on the compact canonical form (C-CF): A = PQ , place both P ∈ ℂ m×r and Q ∈ ℂ r×n have rank r . We will devote effort to something its special case, that is, the compact singular advantage decomposition (C-SVD): A = Ur ( ∑r V*r) , where U*rUr =Ir, V*rVr = Ir ∑r is a angled matrix whose angled elements are the r positive unique values of A in nonincreasing order, Ir is the (r x n) spatial unit matrix, and * designates the Hermitian operator. A exemption based on circumstances of C-SVD for reducing computations is the thin SVD (T-SVD), where we maintain the first t singular principles of A; we thus obtain an (m x n)-spatial rank- t matrix, voice A (t) = Ut (∑t V*t). Notice that an optimal sparse resolution, say xs, guide A(t)will satisfy | support (xs) | ≤ t < r. We wish to minimize ∥b- Ax ∥, place ∥. ∥ denotes any average. Using the C-CF: A = PQ  the proposed treasure consists of following two stages (i) find w minimizing ∥b -Pw∥, perhaps with restraints on e; and, (ii) any solution x of the underdetermined uninterrupted equations Q x = w will be an optimum solution for min ∥b - Ax∥. Hence, in stage (i) lack is not an issue, whereas in stage (ii) ∃α = (α1,…, αr)aforementioned that Q's submatrix whose columns are indexed by α, announce Qα, is regular, otherwise known as nonsingular. The desired sparse answer can be acquired by letting (i) xs =0n, (ii) computing the elementary solution xB ∶= Qα−1w, (iii) allowing xs(α) = xB, and outputting xs. A sparse answer is guaranteed to lie when r < n, i.e., for the underdetermined case (r ≤ m < n), the square case (r ≤ m < n), and the overdetermined case (r ≤ m < n). The proposed invention is most efficient when we wish to minimize the Euclidean average ∥b - Ax ∥2, without some constraints. We could so provide a new authentication of the Moore-Penrose solution, say xMP, written independently by Moore in 1920, Bjerhammar in 1951, and Penrose in 1955. The outline concerning this proof is as follows: (i) calculate w minimizing ∥b - Pw ∥2; and, before (ii) compute  xML = argmin ∥X∥ subject to Q x = w . We so obtain that xMP = xML. However, as proved above we can also compute a sporadic solution xs fulfilling Qxs = w and since also QxMP = w we acquire ∥b - Axs∥2 = ∥b – AxMP∥2, i.e., xs marmalade optimality. The sparse solution xs accordingly obtained concede possibility be preferred over xMP since it gelatin optimality, does not require any growth software, and for one parsimony principle gives a much plainer explanation of the notes. Surprisingly, $\alpha$ can be precomputed from A, then, we only have to calculate w followed by computing the fundamental solution xB∶= Qα−1w; therefore, letting xs = 0n followed by allowing xs (α) = xB  renders the asked xs. In retrospect it turns out that some r independent processions of A say  Aα ∶=  A ((1,…,m) ,α),  suffice to calculate xs while the remaining lines of A can be discarded. Hence, the optimum solution of Aαy + e = b conceivably with constraints on e, voice y, renders xs (α) = y

Author(s) Details:

David Hertz,
Akko, Israel.

Please see the link here: https://stm.bookpi.org/RATMCS-V4/article/view/11818

Forecasting of Long-term Electricity Consumption in China: A Combined Approach based on KPCA and Linear Regression Model | Chapter 8 | Fundamental Research and Application of Physical Science Vol. 6

 In this branch, we demonstrate that the joined model of Kernel Principal Component Analysis (KPCA) and Linear Regression (LR) outperforms other plans, thereby offering a novel and doable approach for long-term power consumption guess. We propose a linked model of KPCA and LR for this purpose. Despite a limited sample intensity, the model can accurately forecast the momentary changes in total electricity consumption, bragging high interpretability and useful utility. We working KPCA to reduce the complicatedness of the original data, before input the dimensionally shortened data into a Backpropagation Neural Network (BPNN) and other models, flexible optimal model results. Visualization of the three principal elements derived through KPCA disclosed that the first principal component represents the unending growth of power consumption, while the other two parts represent the unending fluctuation of power consumption. Additionally, people features, price visage, and industrial building features also cause the increase in China's electricity use, albeit in a changeable manner. Lastly, we foresee that China's total societal power consumption will reach 1.83 heap KWH by 2035, a forecast more optimistic than that of Oxford experts and regular with China's success in combating COVID-19. The model holds an positive view of China's future economic prospects, joining with China's brisk economic growth and allure comprehensive win in the fight against COVID-19.

Author(s) Details:

Zili Huang,
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong-518172, China.

Haochen Zhang,
School of Aerospace Engineering, Tsinghua University, Beijing-100084, China.

Chenxi Qiu,
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong-518172, China.

Jia Liu,
Renm Consulting Company, Beijing-100038, China.

Please see the link here: https://stm.bookpi.org/FRAPS-V6/article/view/10834