Prediction of concrete compressive strength using support vector machine regression and non-destructive testing DOI Creative Commons
Wanmao Zhang, Dunwen Liu,

Kunpeng Cao

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03416 - e03416

Published: June 14, 2024

Performance assessment of existing building structures, especially concrete compressive strength assessment, is a crucial aspect engineering construction for most industrialized countries. Non-destructive testing (NDT) techniques are commonly employed to assess the structures. However, methods predicting using NDT and machine learning do not take into account mix proportion design. This study proposes an effective method predict by combining tests with different designs curing ages. Specifically, support vector regression (SVR) back propagation neural network (BPNN) models established. Furthermore, various evaluation indexes utilized model performance. To construct validate prediction models, total 180 datasets containing specimens ages collected from research literature. The results show that coefficients determination (R2) SVR BPNN test set 86.0 % 86.7 without considering R2 higher than 95 when effects design age. ranged between 92 97 %. All better those model. Consequently, can be accurately evaluate during structural performance buildings.

Language: Английский

Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser DOI Creative Commons
Abidhan Bardhan, Raushan Kumar Singh, Sufyan Ghani

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(14), P. 3064 - 3064

Published: July 11, 2023

The criteria for measuring soil compaction parameters, such as optimum moisture content and maximum dry density, play an important role in construction projects. On sites, base/sub-base soils are compacted at the optimal to achieve desirable level of compaction, generally between 95% 98% density. present technique determining parameters laboratory is a time-consuming task. This study proposes improved hybrid intelligence paradigm alternative tool method estimating density soils. For this purpose, advanced version grey wolf optimiser (GWO) called GWO (IGWO) was integrated with adaptive neuro-fuzzy inference system (ANFIS), which resulted high-performance model named ANFIS-IGWO. Overall, results indicate that proposed ANFIS-IGWO achieved most precise prediction (degree correlation = 0.9203 root mean square error 0.0635) 0.9050 0.0709) outcomes suggested noticeably superior those attained by other ANFIS models, built standard GWO, Moth-flame optimisation, slime mould algorithm, marine predators algorithm. geotechnical engineers can benefit from newly developed during design stage civil engineering MATLAB models also included parameters.

Language: Английский

Citations

27
Predicting tunnel water inflow using a machine learning-based solution to improve tunnel construction safety DOI
Arsalan Mahmoodzadeh,

Hossein Ghafourian,

Adil Hussein Mohammed

et al.

Transportation Geotechnics, Journal Year: 2023, Volume and Issue: 40, P. 100978 - 100978

Published: March 16, 2023

Language: Английский

Citations

24
Hybrid bio-inspired metaheuristic approach for design compressive strength of high-strength concrete-filled high-strength steel tube columns DOI
Masoud Ahmadi, Mehdi Ebadi Jamkhaneh, Ahmad Dalvand

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(14), P. 7953 - 7969

Published: Feb. 23, 2024

Language: Английский

Citations

16
Development of a prediction tool for the compressive strength of ternary blended ultra-high performance concrete using machine learning techniques DOI
Rakesh Kumar,

Shubhum Prakash,

Baboo Rai

et al.

Journal of Structural Integrity and Maintenance, Journal Year: 2024, Volume and Issue: 9(3)

Published: July 2, 2024

Language: Английский

Citations

16
Prediction of concrete compressive strength using support vector machine regression and non-destructive testing DOI Creative Commons
Wanmao Zhang, Dunwen Liu,

Kunpeng Cao

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03416 - e03416

Published: June 14, 2024

Performance assessment of existing building structures, especially concrete compressive strength assessment, is a crucial aspect engineering construction for most industrialized countries. Non-destructive testing (NDT) techniques are commonly employed to assess the structures. However, methods predicting using NDT and machine learning do not take into account mix proportion design. This study proposes an effective method predict by combining tests with different designs curing ages. Specifically, support vector regression (SVR) back propagation neural network (BPNN) models established. Furthermore, various evaluation indexes utilized model performance. To construct validate prediction models, total 180 datasets containing specimens ages collected from research literature. The results show that coefficients determination (R2) SVR BPNN test set 86.0 % 86.7 without considering R2 higher than 95 when effects design age. ranged between 92 97 %. All better those model. Consequently, can be accurately evaluate during structural performance buildings.

Language: Английский

Citations

13