scholarly journals Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1068
Author(s):  
Jiajia Xu ◽  
Li Zhou ◽  
Ge He ◽  
Xu Ji ◽  
Yiyang Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Pearson Correlation ◽  
Influential Factors ◽  
Structural Safety ◽  
Percentage Error ◽  
Modeling Framework ◽  
Machine Material ◽  
Proposed Model

Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

scholarly journals A Machine Learning-Assisted Numerical Predictor for Compressive Strength of Geopolymer Concrete Based on Experimental Data and Sensitivity Analysis

2020 ◽  
Vol 10 (21) ◽  
pp. 7726
Author(s):  
An Thao Huynh ◽  
Quang Dang Nguyen ◽  
Qui Lieu Xuan ◽  
Bryan Magee ◽  
TaeChoong Chung ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sensitivity Analysis ◽  
Compressive Strength ◽  
Fly Ash ◽  
Sodium Hydroxide ◽  
Engineering Properties ◽  
Percentage Error ◽  
Geopolymer Concrete ◽  
Statistical Measures

Geopolymer concrete offers a favourable alternative to conventional Portland concrete due to its reduced embodied carbon dioxide (CO2) content. Engineering properties of geopolymer concrete, such as compressive strength, are commonly characterised based on experimental practices requiring large volumes of raw materials, time for sample preparation, and costly equipment. To help address this inefficiency, this study proposes machine learning-assisted numerical methods to predict compressive strength of fly ash-based geopolymer (FAGP) concrete. Methods assessed included artificial neural network (ANN), deep neural network (DNN), and deep residual network (ResNet), based on experimentally collected data. Performance of the proposed approaches were evaluated using various statistical measures including R-squared (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Sensitivity analysis was carried out to identify effects of the following six input variables on the compressive strength of FAGP concrete: sodium hydroxide/sodium silicate ratio, fly ash/aggregate ratio, alkali activator/fly ash ratio, concentration of sodium hydroxide, curing time, and temperature. Fly ash/aggregate ratio was found to significantly affect compressive strength of FAGP concrete. Results obtained indicate that the proposed approaches offer reliable methods for FAGP design and optimisation. Of note was ResNet, which demonstrated the highest R2 and lowest RMSE and MAPE values.

Modeling the Pressure Transverse for Foam Drilling Operation in Vertical Well

2021 ◽  
Author(s):  
Adesina Fadairo ◽  
Gbadegesin Adeyemi ◽  
Kegang Ling ◽  
Vamegh Rasouli ◽  
Adedayo Iroko ◽  
...  
Keyword(s):  
Drill Pipe ◽  
Flow Region ◽  
Percentage Error ◽  
Drilling Operation ◽  
Underbalanced Drilling ◽  
Proposed Model ◽  
Hydraulic Design ◽  
Foam Drilling ◽  
Function Time

Abstract Pressure transverse in foam drilling operation is sensitive and difficult to predict particular at the start of flow that follows the unavoidable shut in due to inevitable procedure of stop and proceed arising from re-connection of additional drilling pipe to further drill depth. The practice in drilling may not enable the flow to attain steadiness flow region before running in the length of drill pipe. Most existing models in the literature for predicting pressure transverse in foam drilling operation only captured the steadiness flow region of the foam drilling operation by keeping out restriction terms induced by accumulation and kinetic for simplicity sake, hence unsteadiness flow region experienced during foam drilling operation was rarely modelled. It is highly expedient to derive a model that evident the unsteadiness region in order to accurately predict pressure transverse, hence sufficiently analyses the well stability during foam drilling operation. In this study, a model for forecasting pressure transverse in foam drilling operation was established considering restriction term caused by accumulation and kinetic that constitute for accurate formulation of hydraulic model that govern flow of foam during underbalanced drilling. By applying the proposed model to a case study reported in literature, pressure transverse at unsteadiness flow region for foam drilling operation can be quantitatively estimated and analyzed. The result obtained in a case study carried out indicates high variance in pressure as function time at the beginning of flow in foam drilling where unsteadiness is promoted before matching up closely with the results obtained from the existing Guo et al 2003 model at the steadiness flow region. The new model has a better accuracy with a percentage error of 0.74% and 6.4% as compared to previous models by Guo et al 2003. The proposed model make possible for drilling engineer to take decision with larger precision during hydraulic design of foam drilling operation and guaranteeing well stability in complex drilling system.

EXPRESS: Dynamics of Musical Success: A Machine Learning Approach for Multimedia Data Fusion

2021 ◽  
pp. 002224372110164
Author(s):  
Khaled Boughanmi ◽  
Asim Ansari
Keyword(s):  
Machine Learning ◽  
Dirichlet Process ◽  
Penalized Splines ◽  
Multimedia Data ◽  
Acoustic Features ◽  
Modeling Framework ◽  
Learning Framework ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Different Types

The success of creative products depends upon the felt experience of consumers. Capturing such consumer reactions requires the fusing of different types of experiential covariates and perceptual data in an integrated modeling framework. In this paper, the authors develop a novel multimodal machine learning framework that combines multimedia data (e.g., metadata, acoustic features and user generated textual data) in creative product settings and apply it for predicting the success of musical albums and playlists. The authors estimate the proposed model on a unique dataset which they collected using different online sources. The model integrates different types of nonparametrics to flexibly accommodate diverse types of effects. It uses penalized splines to capture the nonlinear impact of acoustic features and a supervised hierarchical Dirichlet process to represent crowd-sourced textual tags. It captures dynamics via a state-space specification. The authors show the predictive superiority of the model with respect to several benchmarks. The results illuminate the dynamics of musical success over the past five decades. The authors then use the components of the model for marketing decisions such as forecasting the success of new albums, album tuning and diagnostics, construction of playlists for different generations of music listeners, and contextual recommendations.

scholarly journals Forecasting Wind Speed using Artificial Neural Networks – A Case Study of a Potential Location of Saudi Arabia

2020 ◽  
Vol 173 ◽  
pp. 01004
Author(s):  
Yunus Parvej Faniband ◽  
S. M. Shaahid
Keyword(s):  
Neural Networks ◽  
Saudi Arabia ◽  
Renewable Energy ◽  
Wind Speed ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Support Vector ◽  
Wind Speed Data ◽  
Proposed Model

The growing concerns regarding the depletion of oil/gas reserves and global warming have made it inevitable to seek energy from wind and other renewable energy resources. Forecasting wind speed is a challenging topic and has important applications in the design and operation of wind power systems, particularly grid connected renewable energy systems, and where forecasting wind speed helps in manipulating the load on the grid. Modern machine learning techniques including neural networks have been widely used for this purpose. As per literature, various models for estimating the hourly wind speed one hour ahead and the hourly wind speed data profile one day ahead have been developed. This paper proposes the use of Artificial Intelligence methods (AI) which are most suitable for the prediction and have provided best results in many situations. AI method involves nonlinear (or linear) and highly complex statistical relationships between input and output data, such as neural networks, fuzzy logic methods, Knearest Neighbors algorithm (KNN) and Support Vector Machine (SVM). AI methods are promising alternatives for predicting wind speed and understanding the wind behavior for a particular region. In the present study (as a case-study), hourly average wind speed data of 13 years (1970-1982) of Qaisumah, Saudi Arabia has been used to evaluate the performance of ANN model. This data has been used for training the neural network. ANN is trained multiple times with different number of hidden neurons to forecast accurate wind speed. The efficiency of proposed model is validated by predicting wind speed of the Qaisumah region with the measured data. Mean Square Error (MSE) and mean absolute percentage error (MAPE values) for proposed model are found to be 0.0912 and 6.65% respectively.

scholarly journals A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 241
Author(s):  
Giovanni Cicceri ◽  
Giuseppe Inserra ◽  
Michele Limosani
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
The Great Recession ◽  
Italian Case ◽  
Complementary Approach ◽  
Proposed Model ◽  
Economic Downturns ◽  
Machine Learning Approach ◽  
Short Term Forecasting

In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

2013 ◽  
Vol 433-435 ◽  
pp. 545-549
Author(s):  
Zhi Jie Song ◽  
Zan Fu ◽  
Han Wang ◽  
Gui Bin Hou
Keyword(s):  
Historical Data ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Influential Factors ◽  
Support Vector ◽  
Proposed Model ◽  
Svm Model ◽  
Vector Machines ◽  
Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

scholarly journals Applying catastrophe progression method to evaluate the service quality of cold chain logistics

2020 ◽  
Author(s):  
Hao Zhang ◽  
Yuxin Shi ◽  
Bin Qiu
Keyword(s):  
Service Quality ◽  
Influential Factors ◽  
Cold Chain ◽  
Proposed Model ◽  
Priority Weights ◽  
Catastrophe Progression Method ◽  
Catastrophe Progression ◽  
Logistics Service Quality

Abstract Logistics service quality (LSQ) is one of the key influential factors in the success of an ecommerce business. In view of the complexity of the topic, this paper proposes a novel model for fresh ecommerce cold chain LSQ evaluation based on the catastrophe progression method. In the proposed methodology, first an index system for evaluating the fresh ecommerce cold chain LSQ is established from the perspective of service recipients. Then, the comprehensive weight of each evaluation index is determined using a combination weighting approach based on maximizing deviations and fuzzy set theory. The priority weights and the ranking of the indices are determined using the catastrophe progression method. Finally, the model is applied in a case study of two representative enterprises. The study demonstrates the validity and practical applicability of the proposed model. Also, based on the evaluation results and findings, some improvement suggestions are made for improving the cold chain LSQ of similar kinds of fresh ecommerce companies.

A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia

2019 ◽  
pp. 016555151987764
Author(s):  
Ping Wang ◽  
Xiaodan Li ◽  
Renli Wu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Complete Information ◽  
Classification Performance ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Learning Models ◽  
Proposed Model

Wikipedia is becoming increasingly critical in helping people obtain information and knowledge. Its leading advantage is that users can not only access information but also modify it. However, this presents a challenging issue: how can we measure the quality of a Wikipedia article? The existing approaches assess Wikipedia quality by statistical models or traditional machine learning algorithms. However, their performance is not satisfactory. Moreover, most existing models fail to extract complete information from articles, which degrades the model’s performance. In this article, we first survey related works and summarise a comprehensive feature framework. Then, state-of-the-art deep learning models are introduced and applied to assess Wikipedia quality. Finally, a comparison among deep learning models and traditional machine learning models is conducted to validate the effectiveness of the proposed model. The models are compared extensively in terms of their training and classification performance. Moreover, the importance of each feature and the importance of different feature sets are analysed separately.

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