scholarly journals Modeling the Completion Time of Public School Building Projects Using Neural Networks

2018 ◽  
Vol 3 (12) ◽  
pp. 1266
Author(s):  
Zeyad S. M. Khaled ◽  
Raid S. Abid Ali ◽  
Musaab Falih Hasan
Keyword(s):  
Completion Time ◽  
Prediction Models ◽  
Back Propagation ◽  
School Buildings ◽  
School Building ◽  
Back Propagation Algorithm ◽  
Building Projects ◽  
Financial Status ◽  
Contract Duration ◽  
Work Stoppages

The Ministry of Education in Iraq is confronting a colossal deficiency in school buildings while stakeholders of government funded school buildings projects are experiencing the ill effects of extreme delays caused by many reasons. Those stakeholders are particularly worried to know ahead of time (at contract assignment) the expected completion time of any new school building project. As indicated by a previous research conducted by the authors, taking into account the opinions of Iraqi experts involved with government funded school building projects, nine major causes of delay in school building projects were affirmed through a questionnaire survey specifically are; the contractor's financial status, delay in interim payments, change orders, the contractor rank, work stoppages, the contract value, experience of the supervising engineers, the contract duration and delay penalty. In this research, two prediction models (A and B) were produced to help the concerned decision makers to foresee the expected completion time of typically designed school building projects having (12) and (18) classes separately. The ANN multi-layer feed forward with back-propagation algorithm was utilized to build up the mathematical equations. The created prediction equations demonstrated a high degree of average accuracy of (96.43%) and (96.79%) for schools having (12) and (18) classes, with (R2) for both ANN models of (79.60%) and (85.30%) respectively. It was found that the most influential parameters of both models were the ratio of the sum of work stoppages to the contract duration, the ratio of contractor's financial status to the contract value, the ratio of delay penalty to the total value of contract and the ratio of mean interim payments duration to the contract duration.

A Model with Back-Propagation Algorithm for the Estimation of Irreducible Water Saturation

2011 ◽  
Vol 361-363 ◽  
pp. 445-450 ◽  
Author(s):  
Ping Hua Ma ◽  
Hong Fu Fan ◽  
Ke Li
Keyword(s):  
Prediction Models ◽  
Water Saturation ◽  
Back Propagation ◽  
Superior Performance ◽  
Back Propagation Algorithm ◽  
Irreducible Water Saturation ◽  
Long Time ◽  
Multi Phase Flow ◽  
Conventional Models ◽  
Hidden Layer

As one of the most important reservoir parameters, irreducible water saturation, Swir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, the target of tertiary recovery, is also a function of Swir. In traditionally, Swir is determined by conducting capillary pressure experiments, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of Swir with mathematical models is developed in recent years. The study reported in this paper uses artificial neural network to determine Swir. The optimal model is chosen among 25 simulations, subtilizing different combinations of hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with other conventional models, demonstrating the superior performance of the proposed Swir prediction models.

scholarly journals Application of Multi-Layer Perceptron Classification Model to Predict the Professional Direction of Sports Undergraduates Through Personality Traits

2020 ◽  
Vol 5 (6) ◽  
Keyword(s):  
Personality Traits ◽  
Prediction Models ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Personality Characteristics ◽  
Classification Model ◽  
Back Propagation Algorithm ◽  
Gender Status ◽  
And Gender ◽  
Test Outcomes

Background & Aims: Personality traits play a stable and intrinsic role in the process of sport undergraduates coping with the multiple stresses of classroom academic performance and maintaining extracurricular sport. The purpose of this study is to determine the correlation of multilayer perceptron (MLP)models in predicting gender status and major choice among sport undergraduates. Method: Personality surveys based on the classic Eysenck questionnaire was carried out and MLPs feedforward neural networks with back propagation algorithm were processed by SPSS and cross-validated among the 332 undergraduates. Descriptive analyses and T tests were used to analyze the personality traits of the overall participating subjects. MLP models the original scores of items in the Eysenck Personality Scale were set as covariates, and "gender" and "major" was set to be the predicted output, respectively. Choose the best predictive models from all models. Results: The personality characteristics of subjects were more extroverted (t =20.838, p =0.000) and more neurotic (t =4.892, p =0.000) and unlikely to be psychotic (t =-0.321, p =0.749). The test outcomes are credible suggested by the Lie score (t =-17.679, p =0.000). The top four items that play an important role in predicting the gender are: N67, N28, E22, E1. The most important items of the E and N dimension scales in the "professional" prediction model are in turn: E85, E1 & N66, N28. Conclusions:The type of the personality model is ENql, meaning extroverted, neurotic, unlikely psychotic and trusted in the personality characteristics. The application of MLP prediction models is to help undergraduates in choosing their major more easily

Probability of change orders and the effect on cost and schedule for new public school buildings

2019 ◽  
Vol 26 (6) ◽  
pp. 1087-1104 ◽  
Author(s):  
Pramen P. Shrestha ◽  
Kabindra Kumar Shrestha ◽  
Haileab B. Zeleke
Keyword(s):  
Public School ◽  
Statistical Tests ◽  
School Buildings ◽  
School Building ◽  
Change Orders ◽  
Building Projects ◽  
Content Type ◽  
New School ◽  
New Public ◽  
The Cost

Purpose Change orders (COs) adversely affect the cost and schedule of projects, specifically during the construction phase. COs of 95 new public school building projects contracted by the Clark County School District (CCSD) of Nevada were analyzed to quantify the cost and schedule growth as well as to determine the effect of COs on cost and schedule growth. The paper aims to discuss these issues. Design/methodology/approach The data were collected from CCSD through questionnaire survey. Descriptive statistics and statistical tests were conducted to determine the effect of COs on cost and schedule growth. Findings It was found that the average amount of COs as well as cost and schedule overruns were 5.9, 3.0 and 7.4 percent, respectively. Statistical tests showed that the amount of COs had an adverse effect on schedule growth; schedule overruns in projects with less than 4 percent COs were significantly lower than projects with more than 4 percent COs. Cost overruns did not significantly differ in those two types of projects. The primary contribution of this study is that it provides the tools and the framework for school district engineers to determine the probability of the occurrence of COs as well as the optimum percentage of COs for a minimum effect on cost and schedule growth of new public school buildings. Probability curves were also developed to determine the likelihood of the occurrence of COs, cost growth and schedule growth in these projects. These findings could be used by school districts to avoid or reduce COs in future projects, minimizing the effect on cost and schedule growth during the construction phase. Research limitations/implications The findings and the probabilities curves developed in this study should be used carefully in other cases. These data were specific to the owner, location and types of buildings and generalizing these findings may have negative consequences. Practical implications The practical implications are that this study could provide a tool to school building administrators to determine the probability of having COs as well as cost and schedule overruns and the effects of COs on cost and schedule overruns. To the authors’ best knowledge, no other studies of this type have been conducted previously. Social implications The social implication of this study is it will help to efficiently use the tax payers’ money while building new school buildings. Originality/value This study has collected the hard data of COs, cost and schedule data of CCSD new school building projects. Therefore, the data are from the projects completed by CCSD. So, the paper is written from the original data received from CCSD.

A computer-based cost prediction model for institutional building projects in Nigeria

2014 ◽  
Vol 12 (4) ◽  
pp. 519-530 ◽  
Author(s):  
Kabir Bala ◽  
Shehu Ahmad Bustani ◽  
Baba Shehu Waziri
Keyword(s):  
Prediction Model ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Cost Prediction ◽  
Building Projects ◽  
Content Type ◽  
Significant Difference ◽  
Computer Based ◽  
Propagation Network

Purpose – The purpose of this study was develop a computer-based cost prediction model for institutional building projects in Nigeria through the use of artificial neural network (ANN) technique. The back-propagation network learns by example and provides good prediction to novel cases. Design/methodology/approach – The input variables were derived from related works with modification and advices from professionals through a field survey. Two hundred and sixty completed project data were used for training and development of the ANN model. Back-propagation algorithm using the gradient descent delta learning rule with a learning coefficient of 0.4 was used. The input layer of the model comprised of nine variables; building height, compactness of building, construction duration, external wall area, gross floor area, number of floors, proportion of opening on external walls, location index and time index. Findings – Several multi-layer perceptron networks were developed with varying architecture from which the network 9-7-5-1 was selected. The performance of the model over the validation sample revealed that the model has a mean absolute per cent error of 5.4 per cent and average error of prediction of −2.5 per cent over the sample. The ANN model was considered to be effective for construction cost prediction. Research limitations/implications – The model may not be suitable for other building types because of the uniqueness of such facility even though significant difference is not anticipated for buildings such as commercial and residential. The models were evaluated based on the prediction errors; other means of evaluation were not used. Originality/value – The study thus provides a simple, yet effective means of predicting construction costs of institutional building projects in Nigeria using an ANN model.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

scholarly journals Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2704
Author(s):  
Yunhan Lin ◽  
Wenlong Ji ◽  
Haowei He ◽  
Yaojie Chen
Keyword(s):  
Pitch Angle ◽  
Back Propagation ◽  
Water Jet ◽  
The State ◽  
Model Parameters ◽  
Propagation Time ◽  
Back Propagation Algorithm ◽  
Angle Error ◽  
Landing Point ◽  
Propagation Algorithm

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION

2008 ◽  
Vol 17 (06) ◽  
pp. 1089-1108 ◽  
Author(s):  
NAMEER N. EL. EMAM ◽  
RASHEED ABDUL SHAHEED
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Back Propagation ◽  
Adaptive Mesh ◽  
Learning System ◽  
Back Propagation Algorithm ◽  
Adaptive Smoothing ◽  
Different Types ◽  
Optimal Values

A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

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