• Computers and Concrete
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• 제19권5호
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• pp.515-526
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• 2017

Recent trend is to use the lightweight concrete in the construction industry because it has several advantages over normal weight concrete. The Lightweight concrete can be produced from the industrial waste materials. In South East Asian region, researchers are very keen to use the waste materials such as oil palm shell (OPS) and palm oil clinker (POC) from the palm oil producing industries. Extensive research has been done on lightweight concrete using OPS or POC over the last three decades. In this paper the aggregate properties of OPS and POC are plotted in conjunction with mechanical and structural behavior of OPS concrete (OPSC) and POC concrete (POCC). Recent investigation on the use of crushed OPS shows that OPSC can be produced to medium and high strength concrete. The density of OPSC and POCC is around 20-25% lower than normal weight concrete. Generally, mechanical properties of OPSC and POCC are comparable with other types of lightweight aggregate concrete. It can be concluded from the previous study that OPSC and POCC have the noteworthy potential as a structural lightweight concrete.

• Steel and Composite Structures
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• 제49권6호
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.