Building material prices forecasting based on least square support vector machine and improved particle swarm optimization

Tang, Bi-qiu; Han, Jia Liang; Guo, Guo-feng; Chen, Yi; Zhang, Sai

doi:10.1080/17452007.2018.1556577

Cited by 18 publications

(7 citation statements)

References 44 publications

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“…This aligns with Mustaffa et al [67] research on commodity price forecasting, boasting a MAPE value of 5.5%, and stock prediction with a MAPE of 0.8% [68]. Tang et al [69] estimated building material prices with an MSE of 2.44% and MAPE of 2.11%.…”

Section: Moderator Variable Testsupporting

confidence: 71%

Accuracy Rate of Least Square Support Vector Machine Method and Its Various Modifications: A Forecasting Evaluation on Multi-Type Data

Mahsup,

Febriani,

Syaharuddin

et al. 2024

ISI

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Section: Moderator Variable Testsupporting

confidence: 71%

Accuracy Rate of Least Square Support Vector Machine Method and Its Various Modifications: A Forecasting Evaluation on Multi-Type Data

Mahsup,

Febriani,

Syaharuddin

et al. 2024

ISI

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“…While this supports the earlier observation (section 3.2.1) of few studies concerning the combined use of AI methods/algorithms and concepts, and active and semi-active structural control systems, MR damper, etc. for purposes such as structural control in building and infrastructure structures; examples of forecasting system include particle swarm optimization integrated with support vector machine [74] and vector error correction model [75] for construction material prices forecasting. It was also identified in Fig.…”

Section: Tablementioning

confidence: 99%

Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities

Darko

Chan

Adabre

et al. 2020

Automation in Construction

360

184

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“…The application of optimisation algorithms and in particular meta-heuristics is expanding rapidly in recent years. As a result, several algorithms such as Billiards Optimisation Algorithm (BOA) [13], Red deer algorithm (RDA) [59], Whales Optimisation Algorithm (WOA) [60], Political Optimizer (PO) [61], Pareto Genetic Algorithm-based Collaborative Optimisation (PGACO) [11], and Artificial Bee Colony (ABC) [62] are developed alongside well-known algorithms such as Genetics Algorithm (GA) [63], Particle Swarm Optimisation (PSO) [12], and Improved Particle Swarm Optimisation (IPSO) [64].…”

Section: Application Of Optimization Methods In the Aec Industrymentioning

confidence: 99%

A BIM-Based Solution for the Optimisation of Fire Safety Measures in the Building Design

et al. 2022

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A significant number of injuries and fatalities occur annually due to fire in buildings. The proactive use of fire safety measures at the design stage can potentially lead to a considerable improvement in reducing casualties by assisting occupants in a safe evacuation. In this study, a framework is developed to obtain a set of appropriate fire safety measures while considering their effects on safe evacuation (i.e., increasing the percentage of survived occupants) and budget limits. The framework consists of four phases, namely (1) initial preparation, (2) optimisation with a meta-heuristic algorithm, (3) decision making when the designer should select appropriate measures considering the budget limit, and (4) applying the selected measures into BIM. A binary version of Billiards-inspired Optimisation Algorithm (BOA) is developed and utilised in the optimisation phase. A case study approach is adopted for this research and the performance of the proposed framework is evaluated by implementing it in two case projects: a residential building and a hospital building. The results indicate that the framework is a beneficial approach for designers to modify building designs in terms of safe evacuation at the design stage by using fire safety measures effectively and economically. It is expected that the output of this framework will help decrease fatalities of building users during a fire.

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Building material prices forecasting based on least square support vector machine and improved particle swarm optimization

Cited by 18 publications

References 44 publications

Accuracy Rate of Least Square Support Vector Machine Method and Its Various Modifications: A Forecasting Evaluation on Multi-Type Data

Accuracy Rate of Least Square Support Vector Machine Method and Its Various Modifications: A Forecasting Evaluation on Multi-Type Data

Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities

A BIM-Based Solution for the Optimisation of Fire Safety Measures in the Building Design

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