Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

Maghrebi, Mojtaba; Sammut, Claude; Waller, S. Travis

doi:10.2478/brj-2014-0001

Cited by 11 publications

(5 citation statements)

References 60 publications

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“…The results indicate that predicting with SVM was significantly more accurate. Maghrebi et al (2014) used ANN to predict the duration of a concrete operation by focusing on supply chain parameters of RMC. The model was tested with a real life dataset of a RMC in Sydney metropolitan area which has 17 depots and around 200 trucks.…”

Section: Construction Schedulingmentioning

confidence: 99%

Artificial Neural Networks in Construction Engineering and Management

Waziri¹,

Bala²,

Bustani³

2017

IJAEC

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Artificial Neural Networks has gained considerable application in construction engineering and management in recent time. Over 100 resources published in refereed journals and conference proceedings were screened and reviewed with the view to exploring the trend and new directions of the applications of different ANN algorithms. The study revealed successful applications of ANNs in cost prediction, optimization and scheduling, risk assessment, claims and dispute resolution outcomes and decision making. It was observed that ANN have been applied to problems that are difficult to solve with traditional mathematical and statistical methods. The integration of ANN with other soft computing methods like Genetic Algorithm, Fuzzy Logic, Ant Colony Optimization, Artificial Bee Colony and Particle Swarm Optimization were also explored which generally indicated better results when compared with conventional ANNs. The study provides comprehensive repute of ANN in construction engineering and management for application in different areas for improved accuracy and reliable predictions.

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Section: Construction Schedulingmentioning

confidence: 99%

Artificial Neural Networks in Construction Engineering and Management

Waziri¹,

Bala²,

Bustani³

2017

IJAEC

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“…Machine learning techniques have been widely used in the literature to solve civil engineering predictions and classification problems (29)(30)(31)(32)(33)(34)(35)(36). Also, some researchers used this technique in pavement evaluation (37)(38)(39)(40)(41).…”

Section: Prediction Of Pavement Performance Application Of Support Vector Regression With Different Kernelsmentioning

confidence: 99%

Prediction of Pavement Performance: Application of Support Vector Regression with Different Kernels

Ziari

Maghrebi²,

Ayoubinejad

et al. 2016

Transportation Research Record: Journal of the Transportation R

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The pavement performance model is a basic part of the pavement management system. The prediction accuracy of the model depends on the number of effective variables and the type of mathematical method that is used for modeling the pavement performance. In this paper, the capability of the support vector machine (SVM) method is analyzed for predicting the future of the pavement condition. Five kernel types of SVM algorithm are formed and nine input variables of the proposed models are extracted from the range of effective variables on the pavement condition. The international roughness index is used as the pavement performance index. The results show that the Pearson VII Universal kernel can accurately predict pavement performance in its life cycle.

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“…The validation process of the optimized ANNs model, along with the equations derived from it, are integral for evaluating its generalization performance, averting overfitting, fine-tuning hyperparameters, comparing models, identifying data inconsistencies, and instilling trust and confidence in the model's efficacy [37,38]. This validation stage serves as a critical checkpoint to ensure that the model effectively addresses the designated problem and performs well on unseen data.…”

Section: Testing the Suggested Equation For Pore Pressurementioning

confidence: 99%

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

Mahmoud,

Alzayer,

Panagopoulos

et al. 2024

Processes

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Pore pressure prediction is a critical parameter in petroleum engineering and is essential for safe drilling operations and wellbore stability. However, traditional methods for pore pressure prediction, such as empirical correlations, require selecting appropriate input parameters and may not capture the complex relationships between these parameters and the pore pressure. In contrast, artificial neural networks (ANNs) can learn complex relationships between inputs and outputs from data. This paper presents a new empirical correlation for predicting pore pressure using ANNs. The proposed method uses 42 datasets of well log data, including temperature, porosity, and water saturation, to train ANNs for pore pressure prediction. The trained model, with the Bayesian regularization backpropagation function, predicts the pore pressure with an average absolute percentage error (AAPE) and correlation coefficient (R) of 4.22% and 0.875, respectively. The trained ANN is then used to develop a new empirical correlation that relates pore pressure to the input parameters considering the weights and biases of the optimized ANN model. To validate the proposed correlation, it is applied to a blind dataset, where the model successfully predicts the pore pressure with an AAPE of 5.44% and R of 0.957. The results show that the proposed correlation provides accurate and reliable predictions of pore pressure. The proposed method provides a robust and accurate approach for predicting pore pressure in petroleum engineering applications, which can be used to improve drilling safety and wellbore stability.

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Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

Cited by 11 publications

References 60 publications

Artificial Neural Networks in Construction Engineering and Management

Artificial Neural Networks in Construction Engineering and Management

Prediction of Pavement Performance: Application of Support Vector Regression with Different Kernels

A New Empirical Correlation for Pore Pressure Prediction Based on Artificial Neural Networks Applied to a Real Case Study

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