PMID- 35909210
OWN - NLM
STAT- MEDLINE
DCOM- 20230106
LR  - 20230111
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 30
IP  - 1
DP  - 2023 Jan
TI  - Compressive strength prediction of high-strength oil palm shell lightweight 
      aggregate concrete using machine learning methods.
PG  - 1096-1115
LID - 10.1007/s11356-022-21987-0 [doi]
AB  - Promoting the use of agricultural wastes/byproducts in concrete production can 
      significantly reduce environmental effects and contribute to sustainable 
      development. Several experimental investigations on such concrete's compressive 
      strength ([Formula: see text]) and behavior have been done. The results of 229 
      concrete samples made by oil palm shell ([Formula: see text]) as a lightweight 
      aggregate ([Formula: see text]) were used to develop models for predicting the 
      [Formula: see text] of the high-strength lightweight aggregate concrete 
      ([Formula: see text]). To this end, gene expression programming ([Formula: see 
      text]), adaptive neuro-fuzzy inference system ([Formula: see text]), artificial 
      neural network ([Formula: see text]), and multiple linear regression ([Formula: 
      see text]) are employed as machine learning ([Formula: see text]) and regression 
      methods. The water-to-binder ([Formula: see text]) ratio, ordinary Portland 
      cement ([Formula: see text]), fly ash ([Formula: see text]), silica fume 
      ([Formula: see text]), fine aggregate ([Formula: see text]), natural coarse 
      aggregate ([Formula: see text]), [Formula: see text], superplasticizer ([Formula: 
      see text]) contents, and specimen age are among the nine input parameters used in 
      the developed models. The results show that all [Formula: see text]-based models 
      efficiently predict the [Formula: see text]'s [Formula: see text], which 
      comprised [Formula: see text] agricultural wastes. According to the results, the 
      [Formula: see text] model outperformed the [Formula: see text] and [Formula: see 
      text] models. Moreover, an uncertainty analysis through the Monte Carlo 
      simulation (MCS) method was applied to the prediction results. The growing demand 
      for sustainable development and the crucial role of eco-friendly concrete in the 
      construction industry can pave the way for further application of the developed 
      models due to their superior robustness and high accuracy in future codes of 
      practice.
CI  - (c) 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Ghanbari, Saeed
AU  - Ghanbari S
AD  - Department of Civil Engineering, University of Mazandaran, Babolsar, Iran.
FAU - Shahmansouri, Amir Ali
AU  - Shahmansouri AA
AUID- ORCID: 0000-0003-3320-6892
AD  - Department of Civil Engineering, University of Mazandaran, Babolsar, Iran.
FAU - Akbarzadeh Bengar, Habib
AU  - Akbarzadeh Bengar H
AD  - Department of Civil Engineering, University of Mazandaran, Babolsar, Iran. 
      h.akbarzadeh@umz.ac.ir.
FAU - Jafari, Abouzar
AU  - Jafari A
AD  - University of Michigan and Shanghai Jiao Tong University Joint Institute, 
      Shanghai Jiao Tong University, Shanghai, China.
LA  - eng
PT  - Journal Article
DEP - 20220801
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Coal Ash)
RN  - 059QF0KO0R (Water)
SB  - IM
MH  - Compressive Strength
MH  - *Coal Ash
MH  - *Water
MH  - Computer Simulation
MH  - Machine Learning
OTO - NOTNLM
OT  - Agricultural waste
OT  - High-strength concrete
OT  - Lightweight aggregate concrete
OT  - Machine learning
OT  - Strength prediction
EDAT- 2022/08/01 06:00
MHDA- 2023/01/07 06:00
CRDT- 2022/07/31 23:19
PHST- 2022/01/18 00:00 [received]
PHST- 2022/07/08 00:00 [accepted]
PHST- 2022/08/01 06:00 [pubmed]
PHST- 2023/01/07 06:00 [medline]
PHST- 2022/07/31 23:19 [entrez]
AID - 10.1007/s11356-022-21987-0 [pii]
AID - 10.1007/s11356-022-21987-0 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2023 Jan;30(1):1096-1115. doi: 
      10.1007/s11356-022-21987-0. Epub 2022 Aug 1.