Modeling Road Construction Project Cost in the Philippines Using the Artificial Neural Network Approach

Roxas, Cheryl Lyne C.; Roxas, Nicanor R.; Cristobal, Jerald B.; Hao, Sara Eunice; Rabino, Rachelle Marie G.; Jr., Fulgencio M. Revalde

doi:10.1109/hnicem48295.2019.9072820

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…The validation sets are used to measure network generalization and halt training when generalization stops improving. The test set has no effect on training, so it provides an independent measure of network performance (Roxas and Ongpeng, 2014).…”

Section: Resultsmentioning

confidence: 99%

A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

Al-Tawal

Arafeh

Sweis

2020

ECAM

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PurposeCost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all projects to establish the project's budget. Confidence in these initial estimates is low, primarily due to the limited availability of suitable data, which leads the construction projects to frequently end up over budget. This paper investigated the efficacy of artificial neural networks (ANNs) methodologies in overcoming cost estimation problems in the early phases of the building design process.Design/methodology/approachCost and design data from 104 projects constructed over the past five years in Jordan were used to develop, train and test ANN models. At the detailed design stage, 53 design factors were utilized to develop the first ANN model; then the factors were reduced to 41 and were utilized to develop the second predictive model at the schematic design stage. Finally, 27 design factors available at the concept design stage were utilized for the third ANN model.FindingsThe models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively.Research limitations/implicationsThis paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects.Originality/valueThe ANN approach introduced as a management tool is expected to provide the stakeholders in the engineering business with an indispensable tool for predicting the cost with limited data at the early stages of construction projects.

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Section: Resultsmentioning

confidence: 99%

A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

Al-Tawal

Arafeh

Sweis

2020

ECAM

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“…An ANN model was developed by Arafa and Alqedra [185] to estimate the early-stage building construction costs. The structural costs of building projects were valued by Roxas and Ongpeng [186] employing the ANN approach.…”

Section: Financial Managementmentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

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Optimizing Effort and Cost Estimation: Model Implementation Using Artificial Neural Networks and Taguchi’s Orthogonal Vector Plans

Rankovic,

Ranković,

Ivanovic

et al. 2024

Artificial Intelligence-Enhanced Software and Systems Engineering

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Modeling Road Construction Project Cost in the Philippines Using the Artificial Neural Network Approach

Cited by 4 publications

References 22 publications

A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

A model utilizing the artificial neural network in cost estimation of construction projects in Jordan

Artificial Neural Networks for Sustainable Development of the Construction Industry

Optimizing Effort and Cost Estimation: Model Implementation Using Artificial Neural Networks and Taguchi’s Orthogonal Vector Plans

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