Waste-Based Management of Steel Reinforcement Cutting in Construction Projects

Melhem, Nadeem N.; Maher, Rami A.; Sundermeier, Matthias

doi:10.1061/(asce)co.1943-7862.0002052

Cited by 11 publications

(1 citation statement)

References 57 publications

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“…Berarti pembesian dalam beton bertulang pada sebuah proyek memiliki kontribusi hingga 30% dari total keseluruhan biaya proyek. Selain meningkatkan profitabilitas sebuah proyek, meminimalisir waste pada baja tulangan beton (trim loss) juga menguntungkan secara lingkungan (Melhem et al, 2021). Menurut Global ABC (2019), industri konstruksi pada tahun 2018 berkontribusi atas 36% dari pemakaian energi dan emisi karbondioksida (CO2) sebesar 39% di bumi.…”

Section: Pendahuluanunclassified

Reinforcement Concrete Steel Bar Trim Loss Optimization Using Metaheuristics Particle Swarm Optimization and Symbiosis Organisms Search

Gozal¹,

Prayogo²

2021

duts

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Steel rebars for reinforced concrete usually stated in a form of bar bending schedule. In a project, an inefficient activity of cutting steel rebar can result a waste in the form of trim loss. This can lead to profit loss and cause an impact to environment. The optimization needs to be done in order to minimize the trim loss from the steel rebar cutting. Previously, there has been many studies on steel bar trim loss optimization using metaheuristic methods as well as studies on cutting pattern generator. However, if the steel rebar variation is too many, the cutting pattern generator will produce a large number of cutting patterns. Therefore, this study aims to solve the optimization problem by producing more efficient cutting pattern while still obtaining minimum trim loss at any conditions. The data used in this research is obtained from a real-life office project. In the process, the study will be comparing the performance of both PSO and SOS from each cutting patterns generator and conditions. The performance of the two methods is assessed from its minimum, maximum, average, standard deviation and the convergence graph of each iteration. The result shows that SOS performed better in finding the minimum trim loss on undersupply condition.

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Section: Pendahuluanunclassified