Neural-Network-Centered Approach to Determining Lower Limit of Combined Rate of Overheads and Markup

Chao, Li-Chung; Kuo, Chiang-Pin

doi:10.1061/(asce)co.1943-7862.0001440

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…The life-cycle costs of existing buildings were assessed using the ANN technique by Oduyemi et al [174]. ANN models were employed by Chao and Kuo [175] to estimate the minimum rate of overhead and markup. Shiha [176] utilized artificial neural networks (ANNs) to predict the cost of building materials in the context of the Egyptian construction industry.…”

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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Section: Financial Managementmentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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“…Rong Hua et al [12] examined the strategies among the owner and contractor through a model of dynamic game to aim at mitigating the issue of cutting the corners to reduce cost at execution for the contractor proposed a low price malignantly. L. Chao and C Kou [13] constructed a model to determine the relationship of the probability of winning and project cost, using a neural network theory has assisted to optimize the minimum rate of overhead and markup, in addition, its illustration by bidding cases of Taiwan organizations. Leśniak & Plebankiewicz [14] and Al-Humaidi [15] designed a making-decision model of bid or not bid for construction bidders based on theory of fuzzy, bid/no bid decisions have been analysed depend on the most vital influencing factors, as well as suggested a ranking of various projects to bid on.…”

Section: Literature Reviewmentioning

confidence: 99%

Applying the noncooperative game model for compensation concept in contractor selection process for large-scale projects

Son

Anh

2021

STCE

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Nowadays, the scale of construction projects has been larger and more complex, the tender preparation is often costly to the bidder thus. It is becoming one of the primary barriers for attracting bidder’s involvement, as well as contractor's encouraging high effort. Bid compensation concept is proposed as a reward to foster the bidder participating in a higher endeavor. Game theory is ideal for modeling the dynamics and deriving high-effort strategies for bid compensation. The experiment results have demonstrated the owner can gain benefit by using rational compensation. The sensitivity analysis also shows the interest correlation between the owner and bidders. By choosing a proper strategy based on Nash Equilibrium solutions, both the owner and bidders can reach to win-win situation.

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“…Another area of application is cost estimation. Chao and Kuo (2018) used an ANN model to estimate the minimum rate of overheads and markup, while Moselhi et al (1991) aimed to calculate the optimum markup. Oduyemi et al (2015) modelled the life-cycle costs of existing buildings with the help of ANN.…”

Section: Use Of Ann In Construction Management Studiesmentioning

confidence: 99%

Using Artificial Neural Networks to Model Bricklaying Productivity

Bokor¹,

Florez‐Perez²,

Pesce³

et al. 2021

Proceedings of the 2021 European Conference on Computing in Construction

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The pre-planning phase prior to construction is crucial for ensuring an effective and efficient project delivery. Realistic productivity rates forecasted during preplanning are essential for accurate schedules, cost calculation, and resource allocation. To obtain such productivity rates, the relationships between various factors and productivity need to be understood. Artificial neural networks (ANNs) are suitable for modelling these complex interactions typical of construction activities, and can be used to assist project managers to produce suitable solutions for estimating productivity. This paper presents the steps of determining the network configurations of an ANN model for bricklaying productivity.

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Neural-Network-Centered Approach to Determining Lower Limit of Combined Rate of Overheads and Markup

Cited by 7 publications

References 17 publications

Artificial Neural Networks for Sustainable Development of the Construction Industry

Artificial Neural Networks for Sustainable Development of the Construction Industry

Applying the noncooperative game model for compensation concept in contractor selection process for large-scale projects

Using Artificial Neural Networks to Model Bricklaying Productivity

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