Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)

Youneszadeh, Hessam; Ardeshir, Abdollah; Sebt, Mohammad Hassan

doi:10.28991/cej-2020-03091612

Cited by 11 publications

(3 citation statements)

References 49 publications

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“…The main architectural design methods are explained in combination with architectural structure design principles in literature [18]. According to literature [19], deformation occurs during the construction and operation of engineering buildings due to the influence of many factors. If the deformation exceeds the prescribed limit, it will have an impact on the normal use of buildings, and in severe cases, it will jeopardize building safety and result in significant societal and human losses.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Model of Green Building Materials Based on Grey Neural Networks

Zheng

Wang

2022

Wireless Communications and Mobile Computing

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With the rapid growth of China’s economy, all kinds of large or super-large buildings are constantly emerging, people pay more and more attention to building safety and environmental protection, and the reinforcement technology of green building materials plays an increasingly important role. The design involves a wide range of reinforcement. In the concrete design, it is necessary to comprehensively consider various factors, improve the safety of material structure through effective measures and ways, and realize the stable development of the construction industry. This paper analyzes and discusses the reinforcement design of green building materials. Grey model and NN model are two commonly used models in building prediction. This paper applies it to the reinforcement of green building materials and constructs a combined grey NN model. It combines the advantages of grey theory model and artificial NN model. Through the organic combination of grey theory and NN model, complex uncertain problems can be solved. It will be used in data processing of reinforcement monitoring and can get ideal calculation results. The reinforcement design in this paper can improve the use safety of buildings and realize the stability and sustainable development of the industry.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Model of Green Building Materials Based on Grey Neural Networks

Zheng

Wang

2022

Wireless Communications and Mobile Computing

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“…From this point of view, a distinction is made between ecological and economic sustainability. Studies have also found that realistic cost and time estimates are among the 10 most important success factors in residential building projects [13]. The need to develop infrastructure projects where one of the proofs is economic sustainability was the subject of another study [14].…”

Section: Introductionmentioning

confidence: 99%

Supply Chain Management and Big Data Concept Effects on Economic Sustainability of Building Design and Project Planning

2021

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Building design and project planning face significant challenges in the 21st century. On the one hand, it is the requirement of building users. On the other hand, new possibilities and technologies contribute to the overall efficiency of construction projects. The progress of information and communication technologies is one of the most extensive opportunities that can significantly streamline designing sustainable buildings. The big data concept is increasingly being implemented in all areas of industry. The design of buildings and the entire civil engineering industry is where these processes are becoming automated with the help of information technology. Supply chain management based on information systems represents a great potential in the design of buildings and planning of construction projects in materials procurement. These facts lead to the assumption that the concept of big data and supply chain management systems can effectively influence the sustainable design of buildings. These technologies are expected to improve economic sustainability by reducing costs and shortening materials’ delivery time and construction time. The research is carried out on a sample of construction projects. The level of use of the concept of big data and SCM systems and their impact on economic sustainability in the form of parameters such as construction project costs, delivery time of materials and building construction were examined. Data collection took place through a questionnaire survey. Within the used methods, this research worked data-free but also scaled to the Likert scale. Data processing was performed using Pearson dependence and the use of a correlation coefficient. The main goal of the research was to analyze the impact of using the concept of big data and supply chain management on the cost of the building, the time of delivery of materials and the construction of the building. It was found that the use of SCM and BD has an impact on the monitored parameters, and the correlation rate is high.

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“…ANNs have been used in various research fields, such as physics, medicine, geology, engineering, and specifically construction engineering [2][3][4][5]. In Tehran, ANNs are employed to predict the success of housing projects in the construction phase [6], Modified Neural Network Algorithm (MNNA) is used to optimize the gains of a controller, and a new mathematical modeling is introduced to promote the exploration form [7]. On the other hand, a NN predictive controller based on a genetic algorithm is designed for Automatic Voltage Regulator (AVR) [8].…”

Section: Introductionmentioning

confidence: 99%

Determination of the Compressive Strength of Concrete Using Artificial Neural Network

Ojeda¹,

Bocanegra²,

Huatangari³

2021

Int. j. eng. technol. innov.

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The objective of the work is to estimate the compressive strength of concrete by means of the application of Artificial Neural Networks (ANNs). A database is created with design variables of mixtures of 175, 210, and 280 kgf/cm², which are collected from certified laboratories of soil mechanics and concrete of the city of Jaen. In addition, Weka software is used for the selection of the variables and Matlab software is used for the learning, training, and validation stages of ANNs. Five ANNs are proposed to estimate the compressive strength of concrete at 7th, 14th, and 28th day. The results show that the networks obtain the average error of 4.69% and are composed of an input layer with eleven neurons, two hidden layers with nine neurons each, and the compressive strength of concrete as the output. This method is effective and valid for estimating the compressive strength of concrete as a non-destructive alternative for quality control in the construction industry.

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Predicting Project Success in Residential Building Projects (RBPs) using Artificial Neural Networks (ANNs)

Cited by 11 publications

References 49 publications

Reinforcement Model of Green Building Materials Based on Grey Neural Networks

Reinforcement Model of Green Building Materials Based on Grey Neural Networks

Supply Chain Management and Big Data Concept Effects on Economic Sustainability of Building Design and Project Planning

Determination of the Compressive Strength of Concrete Using Artificial Neural Network

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