PMID- 38460037
OWN - NLM
STAT- MEDLINE
DCOM- 20240419
LR  - 20240419
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 31
IP  - 17
DP  - 2024 Apr
TI  - Optimized kernel extreme learning machine using Sine Cosine Algorithm for 
      prediction of unconfined compression strength of MICP cemented soil.
PG  - 24868-24880
LID - 10.1007/s11356-024-32687-2 [doi]
AB  - Microbially induced calcite precipitation (MICP) is an eco-friendly 
      bio-remediation technology. The unconfined compressive strength (UCS) of MICP 
      cemented soil is an important indicator of repair effectiveness. This study 
      proposes a machine learning technique utilizing the Sine Cosine Algorithm (SCA) 
      to optimize the regularization coefficient C and kernel width gamma of the kernel 
      extreme learning machine (KELM) to predict the UCS of MICP cemented soil. To 
      evaluate the performance of the proposed models, a dataset containing 180 groups 
      of the UCS of MICP cemented soil was obtained. The results obtained by SCA-KELM 
      were compared with those obtained by the Random Forest algorithm (RF), Support 
      Vector Machine (SVM), and KELM. The performance of these models was evaluated by 
      the scores of MAE, RMSE, and R(2). The results indicate that the SCA-KELM 
      algorithm exhibits optimal prediction performance (Total score: 21). After 
      optimizing KELM with SCA, the total score improved by 110%, suggesting that SCA 
      significantly enhances the KELM performance. After model development, the optimal 
      population size for SCA-KELM was determined to be 50. Based on the mutual 
      information test, an innovative method was developed for categorizing factor 
      sensitivity by employing importance scores as the partitioning criterion. This 
      method categorizes the influencing factors into three tiers: high (importance 
      score: 8.03-11.14%), medium (importance score: 5.93-7.25%), and low (importance 
      score: 3.23-5.18%). These results suggest that the proposed SCA-KELM algorithm 
      can be regarded as a powerful tool for predicting the UCS of MICP cemented soil.
CI  - (c) 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Peng, Shuquan
AU  - Peng S
AD  - School of Resources and Safety Engineering, Central South University, Changsha, 
      Hunan, 410083, People's Republic of China.
FAU - Sun, Qiangzhi
AU  - Sun Q
AD  - School of Resources and Safety Engineering, Central South University, Changsha, 
      Hunan, 410083, People's Republic of China.
FAU - Fan, Ling
AU  - Fan L
AUID- ORCID: 0000-0001-8258-8343
AD  - School of Resources and Safety Engineering, Central South University, Changsha, 
      Hunan, 410083, People's Republic of China. pqrfanlinger@csu.edu.cn.
FAU - Zhou, Jian
AU  - Zhou J
AD  - School of Resources and Safety Engineering, Central South University, Changsha, 
      Hunan, 410083, People's Republic of China.
FAU - Zhuo, Xiande
AU  - Zhuo X
AD  - School of Resources and Safety Engineering, Central South University, Changsha, 
      Hunan, 410083, People's Republic of China.
LA  - eng
GR  - Grant no.52174100/National Natural Science Foundation of China/
GR  - no.51674287/National Natural Science Foundation of China/
GR  - Grant No. 2021JJ30834/Natural Science Foundation of Hunan Province/
PT  - Journal Article
DEP - 20240309
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Soil)
RN  - H0G9379FGK (Calcium Carbonate)
SB  - IM
MH  - *Soil
MH  - *Calcium Carbonate
MH  - Compressive Strength
MH  - Algorithms
MH  - Machine Learning
OTO - NOTNLM
OT  - KELM
OT  - MICP
OT  - SCA
OT  - Sensitivity
OT  - UCS
EDAT- 2024/03/09 20:42
MHDA- 2024/04/19 06:43
CRDT- 2024/03/09 11:07
PHST- 2023/09/21 00:00 [received]
PHST- 2024/02/24 00:00 [accepted]
PHST- 2024/04/19 06:43 [medline]
PHST- 2024/03/09 20:42 [pubmed]
PHST- 2024/03/09 11:07 [entrez]
AID - 10.1007/s11356-024-32687-2 [pii]
AID - 10.1007/s11356-024-32687-2 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2024 Apr;31(17):24868-24880. doi: 
      10.1007/s11356-024-32687-2. Epub 2024 Mar 9.