Elastoplastic Analytical Solution for the Stress and Deformation of the Surrounding Rock in Cold Region Tunnels Considering the Influence of the Temperature Field

Yang, Zedong; Xu, Jian‐Xin; Feng, Qiang; Liu, Weiwei; He, Peng; Fu, Shenggang

doi:10.1061/(asce)gm.1943-5622.0002466

Cited by 44 publications

(7 citation statements)

References 30 publications

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“…As is known, the research related to the building sector covers a wide range of subjects from construction safety [47,48] and material analysis [49,50] to energy systems. In this work, the focus was on energy performance analysis that was successfully carried out using sophisticated machine learning.…”

Section: Discussionmentioning

confidence: 99%

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

2023

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Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects.

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Section: Discussionmentioning

confidence: 99%

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

2023

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“…Various authors have shown latitudinal and longitudinal ionosphere responses due to PPEF [60][61][62][63][64]. EEJ variations at different longitudes are due to the underlining effects of local winds, which are responsible for EEJ driving [65][66][67][68][69]. In addition, longitudinal differences in EEJ are caused by the different nature of the propagating diurnal tides, the meridional winds, and the dynamics of the migratory tides [70][71][72][73][74].…”

Section: Discussionmentioning

confidence: 99%

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

et al. 2023

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We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator.

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“…In the lower-level problem, described by ( 12)- (32), constraints (15), ( 16), (19), and ( 23) have a nonlinear framework. Nonlinear equation is not as sum of different variables [93][94][95]. When linearizing Equations ( 15) and ( 16), it should be noted that the voltage amplitude in the electrical transmission network needs to be between 0.95 and 1.05 p.u.…”

Section: Linear Approximation Model Of the Lower-level Problemmentioning

confidence: 99%

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

Dehshiri Badi,

Amir,

Shariatmadar

2023

IET Renewable Power Gen

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This paper presents the generation and transmission expansion planning (GTEP) in electricity and gas networks by considering their resilience against floods and earthquakes. These networks supply electricity, heat, and gas consumption energies as a multi‐carrier microgrid. The scheme is expressed in the form of bi‐level optimization, the upper level of which is the minimization of generation and transmission planning cost (total investment cost and expected operating cost) in the mentioned networks constrained to the investment budget and the planning model of the mentioned elements. Lower‐level formulation minimizes the total expected annual operating cost of these networks and the expected outage cost of electricity, heat, and gas consumers in the event of floods and earthquakes. This formulation is bound by the power flow equations of electricity and gas networks, the operation and resilience constraints of the networks, and the limitation on generation capability. In this problem, the expected energy not‐supplied and the outage cost during natural disasters are considered resilience indicators. Next, a single‐level model for the proposed design is extracted from the Karush–Kuhn–Tucker (KKT) method. The basic requirement of this method is the convexity of the lower‐level constraints. For this purpose, first, a linear approximation model is obtained for the lower‐level constraints of the problem. Furthermore, stochastic optimization is adopted to model the uncertainty of load, renewable power, and network equipment availability during floods and earthquakes. Finally, the extracted numerical results confirm the capability of the proposed scheme in improving the operation and resilience of the mentioned networks using optimal generation and transmission planning.

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Elastoplastic Analytical Solution for the Stress and Deformation of the Surrounding Rock in Cold Region Tunnels Considering the Influence of the Temperature Field

Cited by 44 publications

References 30 publications

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

Resilience‐oriented expansion planning of multi‐carrier microgrid utilizing bi‐level technique

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