A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans

Ko, Yongho; Han, Seungwoo

doi:10.3390/su9040635

Cited by 9 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because these general office buildings are sale-inlots or rent businesses, and cash flow for the owner acts as a major component determining the success and failure of the project, it is important to manage the cash flow smoothly by predicting the real-time construction duration from the project planning phase when feasibility analysis is carried out (Hwang 2002;Ko, Han 2017). However, there is a limit on the information that can be obtained in the project planning phase by the owner.…”

Section: Introductionmentioning

confidence: 99%

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom

Seo

Kim

et al. 2018

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

Abstract. Accurate prediction of the construction duration is imperative to the reliable cash flow analysis during the project planning phase when feasibility analysis is carried out. However, lack of information and frequent changes that occur as a result of a negotiation process between the owner and the designer in defining the project scope make it difficult to compute real-time construction duration. Domestic and foreign models for calculating the construction durations cannot be readily applied to computation of construction duration for general office buildings in Korea specifically during the project planning phase as there is a limit in its applicability due to numerous restrictions. Moreover, there are no preceding studies suggesting different computational approaches to predict the entire construction duration for office buildings with the approximate construction duration concept during planning phase. Therefore, based on the collected performance data, this study proposes a multiple linear regression model that facilitates reliable prediction of approximate construction duration for office buildings in the project planning phase. The model will allow the owner and other stakeholders to predict the real-time construction duration using the basic information on office buildings and to assess the construction durations incorporating frequent changes during the project planning phase.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Yeom

Seo

Kim

et al. 2018

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

show abstract

“…In addition, we added a third test called Mean Absolute Percentage Error (MAPE) [37] to measure the average differences between system-generated and human-generated scores. This evaluation can also measure the success rate of ASAS [16].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The MAPE equation is shown in (7), where x (i) is the score from the lecturer and y (i) is the system-generated score, while n is the number of the data. After the mean absolute error using the MAPE equation is obtained, we can also get the Percentage Accuracy (PA) [37] by subtracting the value of 100 with the MAPE value, as shown in (8):…”

Section: Performance Evaluationmentioning

confidence: 99%

Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

2022

View full text Add to dashboard Cite

Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two sentences with significantly different word lengths. This problem has been handled by the Geometric Average Normalized-Longest Common Subsequence (GAN-LCS) method by eliminating non-contributive words utilizing the Longest Common Subsequence method. However, students’ answers may vary not only in character length but also in the words they choose. For instance, some students tend only to write the abbreviations or acronyms of the phrase instead of writing meaningful words. As a result, it will reduce the intersection character between the reference answer and the student answer. Moreover, it can change the sentence structure even though it has the same meaning by definition. Therefore, this study aims to improve GAN-LCS method performance by incorporating the abbreviation checker to handle the abbreviations or acronyms found in the reference answer or student answer. The dataset used in this study consisted of 10 questions with 1 reference answer for each question and 585 student answers. The experimental results show an improvement in GAN-LCS performance that could run 34.43% faster. Meanwhile, the Root Mean Square Error (RSME) value became lower by 7.65% and the correlation value was increased by 8%. Looking forward, future studies may continue to investigate a method for automatically generate the abbreviations dictionary.

show abstract

“…During the training session, the error between the actual output and the target is calculated and then the weights in all layers are being updated until the error reaches an acceptable level [128]. The back-propagation algorithm uses several cost functions as the evaluation metrics in forecasting accuracy through correlation and error analysis between the target and estimated value [36].…”

Section: Prediction Evaluation Metricsmentioning

confidence: 99%

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

et al. 2021

View full text Add to dashboard Cite

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

show abstract

A Duration Prediction Using a Material-Based Progress Management Methodology for Construction Operation Plans

Cited by 9 publications

References 26 publications

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Development of an Approximate Construction Duration Prediction Model During the Project Planning Phase for General Office Buildings

Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Contact Info

Product

Resources

About