Assessing effects of economic factors on construction cost estimation using deep neural networks

Wang, Ran; Asghari, Vahid; Cheung, Clara; Hsu, Shu-Chien; Lee, Chia Jung

doi:10.1016/j.autcon.2021.104080

Cited by 27 publications

(8 citation statements)

References 49 publications

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“…At the final step of the research, SHAP analysis was performed to enhance the explainability of the ML model. Model interpretability is an essential element of prediction studies and highlighted by many scholars [43][44][45]. SHAP summary plot is illustrated in Figure 6.…”

Section: Resultsmentioning

confidence: 99%

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

Koç¹

2023

JCEMI

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Employees working on construction projects have a higher risk of experiencing occupational accidents compared to workers in other sectors. Newly employed workers might face an occupational accident shortly after they start working due to not being able to notice risky environmental conditions. Despite existing studies in construction safety literature focusing on several output variables such as fatality, accident type or accident severity predictions, no studies have examined the short-term susceptibility of construction workers to occupational injuries. This study aims to develop a model to predict construction workers' susceptibility to short-term occupational accidents using interpretable machine learning (ML) methods. Hence, the primary research objective is to identify construction workers who have high probability of experiencing an occupational accident shortly after their employment. In this respect, a national dataset of occupational accidents encountered in the construction industry in Turkey was collected and subjected to various pre-processing elements (data cleaning, data scaling, and data resampling) to prepare the data for prediction. At the processing step, Stochastic Gradient Boosting (SGB) algorithm was applied for the classification purpose.In the next step, Shapley Additive Explanations (SHAP) was used as an interpretable artificial intelligence algorithm to explain how, to what extent, and in which direction the input variables affect the prediction scheme, which is another distinguishing feature of the present study compared to past studies in the subject matter. Results show that the proposed SGB model is a powerful detector for the classification problem and salary of workers, past accident in the company, and number of workers in the company were the most influencing factors. Overall, this study contributes to practice by improving the safety conditions of the newly employed workers as well as minimizing their accident probability through intensified safety training. Given that contemporary safety management applications demand a new set of data-driven inputs, proposed model is expected to help industry professionals and safety managers apply more robust safety risk mitigation and/or prevention measures.

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Section: Resultsmentioning

confidence: 99%

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

Koç¹

2023

JCEMI

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“…Cost of investment plays a major role in TEA of any plant involved in the manufacturing of a particular product or providing a dedicated service. A proper cost estimation reduces the risk of financial losses in an organization [16]. In the present study, TEA is conducted to estimate the prime cost, works cost, administration cost, production cost and the cost of sale incurred for a plant that manufactures 250 PRBs of 5-7 kW on daily basis.…”

Section: Cost Estimation and Calculationmentioning

confidence: 99%

Techno-Economic Assessment of LPG operated Porous Radiant Burner based Cook-stoves for Commercial Cooking Applications

Shaik

Muthukumar

Kalita

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The present study is to conduct a Techno-economic Assessment of a 5-7 kW Porous Radiant Burner (PRB) – based LPG cook-stove operating in eateries and restaurants. Process flow study has been performed primarily to understand the technological process involved in the PRB and also to estimate the cost of manufacturing. The cost estimation has been done considering direct costs such as material costs and labor costs, and indirect costs such as factory costs, works costs and administration costs. Sensitivity analysis has been performed considering the production capacity of 50, 250 and 500 cook-stoves each day. An economic feasibility study of PRB cook-stoves has been done by estimating the Pay Back Period, Net Present Value and Internal Rate of Return. For a manufacturing capacity of 250 cook-stoves per day, PRB yields a 51.4% return on the investment with a payback period of 0.68 years. Adopting PRB-based cook-stoves in small, medium and large-scale eateries results in an annual saving of Rs. 77500, Rs. 235000 and Rs. 1170000, indicating that PRB-based cook-stoves are undoubtedly economically viable.

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“…Recently, computer-aided algorithms have been successfully conducted in construction management studies. AI models can handle the complexity and nonlinearity of construction projects and help the project's parties understand the uncertainties and incomplete information at the early stage of the construction process [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the current method explores the linear relationship between input and output predictors. Based on that, exploring an advanced method that can investigate the complex system of cost estimation parameters is very important to achieve accurate results [38]. Furthermore, integrating a new feature selection method with AI algorithms is significant for construction management engineering to get accurate prediction performance [60].…”

Section: Introductionmentioning

confidence: 99%

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

et al. 2022

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Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence (AI) models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three AI models, including random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for cost estimation. Datasets were gathered based on a survey conducted on 90 building projects in Iraq. Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost-RF using six input parameters, with r-squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. The comparison results revealed that all AI models showed good prediction performance when applied to datasets affected by more than two parameters. The outcomes of this research revealed an optimistic strategy that can help decision makers select the influencing parameters in the early phases of project management. Also, developing a prediction model with high precision results can assist the project’s estimators in decreasing the errors in the cost estimation process.

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Assessing effects of economic factors on construction cost estimation using deep neural networks

Cited by 27 publications

References 49 publications

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

Determining the Short-term Susceptibility of Construction Workers to Occupational Accidents Using Stochastic Gradient Boosting

Techno-Economic Assessment of LPG operated Porous Radiant Burner based Cook-stoves for Commercial Cooking Applications

Developing an Integrative Data Intelligence Model for Construction Cost Estimation

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