Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks
In recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) b...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6518147 |
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| _version_ | 1849307558706151424 |
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| author | Jiacheng Dong Yuan Chen Gang Guan |
| author_facet | Jiacheng Dong Yuan Chen Gang Guan |
| author_sort | Jiacheng Dong |
| collection | DOAJ |
| description | In recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) belongs to a time series network, and the purpose of timeliness transfer calculation is achieved through the weight sharing of time steps. The long-term and short-term memory neural network (LSTM NN) solves the RNN limitations of the gradient vanishing and the inability to address long-term dependence under the premise of having the above advantages. The present study proposed a new framework based on LSTM, so as to explore the applicability and optimization mechanism of the algorithm in the field of cost indexes prediction. A survey was conducted in Shenzhen, China, where a total of 143 data samples were collected based on the index set for the corresponding time interval from May 2007 to March 2019. A prediction framework based on the LSTM model, which was trained by using these collected data, was established for the purpose of cost index predictions and test. The testing results showed that the proposed LSTM framework had obvious advantages in prediction because of the ability of processing high-dimensional feature vectors and the capability of selectively recording historical information. Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. This research extended current algorithm tools that can be used to forecast cost indexes and evaluated the optimization mechanism of the algorithm in order to improve the efficiency and accuracy of prediction, which have not been explored in current research knowledge. |
| format | Article |
| id | doaj-art-1ff714067ae242559f4dd765e1766206 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-1ff714067ae242559f4dd765e17662062025-08-20T03:54:43ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/65181476518147Cost Index Predictions for Construction Engineering Based on LSTM Neural NetworksJiacheng Dong0Yuan Chen1Gang Guan2School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Civil Engineering, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Civil Engineering, Zhengzhou University, Zhengzhou 450001, ChinaIn recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) belongs to a time series network, and the purpose of timeliness transfer calculation is achieved through the weight sharing of time steps. The long-term and short-term memory neural network (LSTM NN) solves the RNN limitations of the gradient vanishing and the inability to address long-term dependence under the premise of having the above advantages. The present study proposed a new framework based on LSTM, so as to explore the applicability and optimization mechanism of the algorithm in the field of cost indexes prediction. A survey was conducted in Shenzhen, China, where a total of 143 data samples were collected based on the index set for the corresponding time interval from May 2007 to March 2019. A prediction framework based on the LSTM model, which was trained by using these collected data, was established for the purpose of cost index predictions and test. The testing results showed that the proposed LSTM framework had obvious advantages in prediction because of the ability of processing high-dimensional feature vectors and the capability of selectively recording historical information. Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. This research extended current algorithm tools that can be used to forecast cost indexes and evaluated the optimization mechanism of the algorithm in order to improve the efficiency and accuracy of prediction, which have not been explored in current research knowledge.http://dx.doi.org/10.1155/2020/6518147 |
| spellingShingle | Jiacheng Dong Yuan Chen Gang Guan Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks Advances in Civil Engineering |
| title | Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks |
| title_full | Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks |
| title_fullStr | Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks |
| title_full_unstemmed | Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks |
| title_short | Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks |
| title_sort | cost index predictions for construction engineering based on lstm neural networks |
| url | http://dx.doi.org/10.1155/2020/6518147 |
| work_keys_str_mv | AT jiachengdong costindexpredictionsforconstructionengineeringbasedonlstmneuralnetworks AT yuanchen costindexpredictionsforconstructionengineeringbasedonlstmneuralnetworks AT gangguan costindexpredictionsforconstructionengineeringbasedonlstmneuralnetworks |