Neural Network‐Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Car-Pušić, Diana; Petruseva, Silvana; Pancovska, Valentina Zileska; Zafirovski, Zlatko

doi:10.1155/2020/8886170

Cited by 11 publications

(5 citation statements)

References 36 publications

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“…Recently, due to the success of deep learning, MLP has gained renewed attention. MLP is often used as a back propagation algorithm for learning, which is regarded as a standard supervised learning algorithm in the eld of pattern recognition, and continues to become a subject of research in the eld of computational neurology and parallel distributed processing [30]. At present, MLP has been proved to be a general functional approximation method, which can be used to t complex functions, or solve classi cation problems.…”

Section: Discussionmentioning

confidence: 99%

Predicting Hospitalization Costs for Pulmonary Tuberculosis Patients Based on Machine Learning

Fan,

Abulizi,

You

et al. 2024

Preprint

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Background: Pulmonary tuberculosis is a prevalent chronic disease associated with a significant economic burden on patients. Predicting costs can help rationalize the cost structure and manage expenses efficiently. Traditional models have limited predictive performance, but machine learning and big data analysis have shown promise in predicting hospitalization costs. By utilizing accurately predicting costs, medical resources could be allocated more effectively, thereby leading to better control of patient hospitalization costs. Methods: This study selected data from the information system of a pulmonary hospital in the Kashgar between 2020 and 2022. A total of 9570 eligible pulmonary tuberculosis patients were included in the study. Multiple regression and Multilayer Perceptron (MLP) prediction models were developed using SPSS 26.0 and Python 3.7, respectively. The training set included data from 2020 and 2021, while the test set comprised data from 2022. The models predicted seven costs related to pulmonary tuberculosis patients, including diagnostic cost, medical service cost, material cost, treatment cost, drug cost, other cost, and total hospitalization cost. The model's predictive performance was evaluated using R-square, Root Mean Squared Error, and Mean Absolute Error metrics. Results: Among the 9570 pulmonary tuberculosis cases included in the study, the median and quartile of patient age are 67.00 (55.00, 74.00) years old; The median and quartile length of hospital stay for patients are 14.00 (11.00, 21.00) days, and the median and quartile of total hospitalization expenses are 13150.45 (9891.3419648.48) yuan. Material cost accounted for the highest proportion (30.25%) in the composition of total hospitalization cost, followed by drug cost (24.01%). Nine factors, including age, marital status, admission condition, length of hospital stay, initial treatment, presence of other diseases, transfer, drug resistance, and admission department, significantly influenced hospitalization costs for pulmonary tuberculosis patients. Excluding other cost, in both the training and test sets, the MLP model demonstrated higher R2 and lower RMSE and MAE results. Conclusion: In conclusion, MLP can effectively leverage patient information and accurately predict various hospitalization costs, achieving a rationalized structure of hospitalization costs by adjusting higher-cost inpatient items and balancing different cost categories. The insights from this predictive model also hold relevance for research in other medical conditions.

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Section: Discussionmentioning

confidence: 99%

Predicting Hospitalization Costs for Pulmonary Tuberculosis Patients Based on Machine Learning

Fan,

Abulizi,

You

et al. 2024

Preprint

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“…Subsequently, the price offer provided by the construction company was also rejected by the insurance company. Where modern methods and tools are not used for pricing construction work and checking the extent of damage [36][37][38][39][40], there is no other recourse for insurance companies but to conduct detailed on-site inspections and carefully scrutinise repair budgets.…”

Section: Building D -Apartment Building -Roof Repairmentioning

confidence: 99%

Determination of the extent of damage and calculation of the indemnity in case of natural disaster – tornado in South Moravia

2023

Archives of Civil Engineering

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In June 2021, a tornado struck a large area in southern Moravia, causing extensive damage to property owned by individuals and legal entities. A need thus arose to speed up the process of estimating the amount of insurance indemnity. This process involved local inspections and subsequent assessment of quotations from construction companies for repairs, as the adjusters did not have the time and resources to estimate the amount of damage using the usual method, i.e. an itemised budget containing a list of works, supplies and services necessary to restore a structure to its original condition based on an on-site inspection. This article contains a retrospective analysis of the accepted quotations and evaluates differences in terms of scope and price compared to the standard procedure. Four apartment buildings were selected for assessment of the insulation and roof repairs. The results show that there are clear discrepancies between the price as determined by the itemised budget using the usual prices and the construction companies' quotations. The analysis of the selected buildings has indicated that the quotations can by no means be accepted without first establishing the total damage and its actual scope. Major damage caused by a natural disaster will still have to be estimated on the basis of a personal inspection of the damaged property and preparation of an itemised budget created in line with the applicable pricing system.

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“…Some machine learning models (regression neural network (RNN), genetic algorithm (GA), genetic programming) are the potential to handle natural relationships among the variable for predicting expected outcomes. The RNN model can be used for nonlinear relationships among the numerical variables for the project's cost prediction (Car-Pusic et al 2020;Yip et al 2014). While the model performs better even for limited data sets, numerical cost data from past similar projects should be used.…”

Section: Towards Potential Cost Overrun Prediction Models For Tpppsmentioning

confidence: 99%

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

Islam

Mohandes

Mahdiyar

et al. 2022

J. Constr. Eng. Manage.

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Globally, power projects are prone to cost overrun projects. Within the body of knowledge, previous studies have paid less attention to predicting the cost overruns to assist contingency cost planning. Particularly, the thermal power plant projects (TPPPs), the enormous risks involved in their delivery undermine the accuracy of cost overrun prediction. To prevent cost overrun in thermal plant projects, these risks need to be accounted for by employing sophisticated cost overrun prediction techniques. This study aims to develop a Hybrid Predictive-Probabilistic-based Model (HPPM) that integrates a genetic programming technique with a Monte Carlo simulation (MCS). The HPPM was proposed based on the data collected from TPPPs in Bangladesh. Also, the sensitivity of the HPPM was examined to identify the critical risks in cost overruns simulation.The simulation outcomes show that 40.48% of a project's initial estimated budget was the most probable to cost overrun, while the maximum cost overrun will not exceed 75% with 90% of confidence. Practically, the analysis will sensitize project managers to emphasize thermal plants' budget accuracy not only at the initial project delivery phase but throughout the project lifecycle.Theoretically, the HPPM model could be employed for cost overrun prediction in other types of power plant projects.

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Neural Network‐Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System

Cited by 11 publications

References 36 publications

Predicting Hospitalization Costs for Pulmonary Tuberculosis Patients Based on Machine Learning

Predicting Hospitalization Costs for Pulmonary Tuberculosis Patients Based on Machine Learning

Determination of the extent of damage and calculation of the indemnity in case of natural disaster – tornado in South Moravia

A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects

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