Regression models for estimating ultimate and serviceability limit states of underground rock caverns

System reliability assessment on deep braced excavation adjacent to an existing upper slope in mountainous terrain: a case study

“…It has been verified by Lian et al [16] that the SSR FE method can be widely applied in the engineering practice. In addition, Cheng et al [4], Goh and Zhang [9], Ishii et al [12], Zhang and Goh [30], Tschuchnigg et al [24], Oberhollenzer et al [21], Gao et al [6], Schneider-Muntau et al [23], Dyson and Tolooiyan [5] have also demonstrated that the SSR technique performs well in the many slope cases analysed.…”

Application of neural networks for the reliability design of a tunnel in karst rock mass

“…Many of these suggest that the NN models are superior to the geostatistical Kriging model and exhibit higher accuracy, e.g., in geodesy (Akcin and Celik 2013); groundwater contamination (Chowdhury et al 2010); ionosphere mapping, especially when data set is spare (Jiang et al 2015); geotechnical site characterization (Samui and Sitharam 2010) or mapping of rock depth below soft deposits (Sitharam et al 2008 conditions prior to excavation when compared with the methods based on soft computing methods, while Santos et al (2015) concluded that model errors obtained with the different estimation methods (linear regression, geostatistical Kriging, and NN algorithms) are very similar. As the utilization of NN generated using evolutionary algorithms can be considered as an advanced surrogate model, compared to the traditionally used statistical and experimental methods, many researchers utilized its benefits in rock tunneling and underground rock engineering (Lee and Sterling 1992;Moon et al 1995;Benardos and Kaliampakos 2004;Yoo and Kim 2007;Mahdevari and Torabi 2012;Zhang and Goh 2015;Hasegawa et al 2019).…”

Optimization of geometric parameters of soil nailing using response surface methodology

“…Sivakumar Babu and Basha (2008) introduced a target reliability approach for design optimization of concrete cantilever retaining walls; an optimization design method of composite soil nailing in loess excavation was introduced by Chang (2009); Ahmadi-Nedushan and Varaee (2009) proposed an optimization algorithm based on the particle swarm optimization (PSO) for optimum design of reinforced concrete Earth-retaining walls; Ghazavi and Bonab (2011) applied a methodology to arrive at the optimal design of concrete retaining wall using the ant colony optimization (ACO); Deb and Dhar (2011) proposed a finite difference-based simulation model and an evolutionary multiobjective optimization model to identify the optimal parameters for granular bed-stone column improved soft soil; Telis et al (2013) presented a method for simultaneous optimization of the design characteristics of an Earth-retaining structure design using quality tools; Seo et al (2014) estimated three design variables from the optimization design procedure proposed in soil nailing study considering constrained conditions; Zhang and Goh (2015 ) used a regression models for estimating ultimate and serviceability limit states of underground rock caverns; Yuan et al (2019) proposed a statistical characterization of model which uncertainty has been exclusively focused on reliability-based design of soil nail wall; for reinforced concrete retaining wall design, proposed an optimization algorithm called Rao-3; the total weight of the steel and concrete, which were used for constructing the retaining wall, were chosen as the objective function; used Grey wolf optimization algorithm of reinforced concrete cantilever retaining wall design.…”