Projectile Penetration into Calcareous Sand Subgrade Airport Runway Pavement with Genetic Algorithm Optimization
As an important civil and military infrastructure, airport runway pavement is faced with threats from cluster munitions, since it is vulnerable to projectile impacts with internal explosions. Aiming at the damage assessment of an island airport runway pavement under impact, this work dealt with disc...
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description | As an important civil and military infrastructure, airport runway pavement is faced with threats from cluster munitions, since it is vulnerable to projectile impacts with internal explosions. Aiming at the damage assessment of an island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and the calcareous sand subgrade multi-layer structure. First, the Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features, like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, the subsequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete, as well as subgrade calcareous sand. Prior to the multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error ≤ 5.6% providing a reliable numerical tool for deep penetration damage assessments. Third, projectile penetration into the airport runway structure with concrete pavement and calcareous sand subgrade was evaluated with validated DEM model. Penetration numerical simulations with various projectile weight, pavement concrete thickness as well as striking velocity, were performed to achieve the DOP. Moreover, the back-propagation (BP) neural network proxy model was constructed to predict the airport runway penetration data with good agreement realizing rapid and robust DOP forecasting. Finally, the genetic algorithm was coupled with the proxy model to realize intelligent optimization of pavement penetration, whereby the critical velocity projectile just perforates concrete pavement indicating the severest subsequent munition explosion damage. |
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Aiming at the damage assessment of an island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and the calcareous sand subgrade multi-layer structure. First, the Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features, like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, the subsequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete, as well as subgrade calcareous sand. Prior to the multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error ≤ 5.6% providing a reliable numerical tool for deep penetration damage assessments. Third, projectile penetration into the airport runway structure with concrete pavement and calcareous sand subgrade was evaluated with validated DEM model. Penetration numerical simulations with various projectile weight, pavement concrete thickness as well as striking velocity, were performed to achieve the DOP. Moreover, the back-propagation (BP) neural network proxy model was constructed to predict the airport runway penetration data with good agreement realizing rapid and robust DOP forecasting. Finally, the genetic algorithm was coupled with the proxy model to realize intelligent optimization of pavement penetration, whereby the critical velocity projectile just perforates concrete pavement indicating the severest subsequent munition explosion damage.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma17235696</identifier><identifier>PMID: 39685131</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Airports ; Algorithms ; Asphalt pavements ; Back propagation networks ; Cluster bombs ; Computer simulation ; Concrete ; Concrete pavements ; Concrete slabs ; Critical velocity ; Damage assessment ; Deformation ; Discrete element method ; Error analysis ; Experiments ; Explosions ; Genetic algorithms ; Granular materials ; Islands ; Military supplies ; Multilayers ; Numerical analysis ; Optimization ; Pavement construction ; Penetration depth ; Projectiles ; Reinforced concrete ; Rigid pavements ; Runways ; Sand ; Simulation ; Simulation methods ; Strain hardening ; Subgrades ; Terminal ballistics ; Thickness ; Threat evaluation</subject><ispartof>Materials, 2024-11, Vol.17 (23), p.5696</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Third, projectile penetration into the airport runway structure with concrete pavement and calcareous sand subgrade was evaluated with validated DEM model. Penetration numerical simulations with various projectile weight, pavement concrete thickness as well as striking velocity, were performed to achieve the DOP. Moreover, the back-propagation (BP) neural network proxy model was constructed to predict the airport runway penetration data with good agreement realizing rapid and robust DOP forecasting. Finally, the genetic algorithm was coupled with the proxy model to realize intelligent optimization of pavement penetration, whereby the critical velocity projectile just perforates concrete pavement indicating the severest subsequent munition explosion damage.</description><subject>Airports</subject><subject>Algorithms</subject><subject>Asphalt pavements</subject><subject>Back propagation networks</subject><subject>Cluster bombs</subject><subject>Computer simulation</subject><subject>Concrete</subject><subject>Concrete pavements</subject><subject>Concrete slabs</subject><subject>Critical velocity</subject><subject>Damage assessment</subject><subject>Deformation</subject><subject>Discrete element method</subject><subject>Error analysis</subject><subject>Experiments</subject><subject>Explosions</subject><subject>Genetic algorithms</subject><subject>Granular materials</subject><subject>Islands</subject><subject>Military supplies</subject><subject>Multilayers</subject><subject>Numerical analysis</subject><subject>Optimization</subject><subject>Pavement construction</subject><subject>Penetration depth</subject><subject>Projectiles</subject><subject>Reinforced concrete</subject><subject>Rigid pavements</subject><subject>Runways</subject><subject>Sand</subject><subject>Simulation</subject><subject>Simulation methods</subject><subject>Strain hardening</subject><subject>Subgrades</subject><subject>Terminal ballistics</subject><subject>Thickness</subject><subject>Threat evaluation</subject><issn>1996-1944</issn><issn>1996-1944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkV9LHDEUxUOpqGx98QNIwJdSWDuZZPLncVmsLQgu6vuQTW62WWaSMZNx0U_fbNfW0tyHhMvvHm7OQeicVFeUquprr4moacMV_4BOiVJ8ThRjH_95n6CzcdxW5VBKZK2O0QlVXDaEklM0rFLcgsm-A7yCADnp7GPAPuSIl7ozOkGcRvygg8UP03qTtAW88GmIKeP7Kez0C17pZ-ghZLzz-Se-2ct4gxfdJqbS6PHdkH3vX38rf0JHTncjnL3dM_T47fpx-X1-e3fzY7m4nZtaqDwnQtjaVVZZIaVlDW-44EzztaoVOGMbRmRjJWPGmQqcoNI6pzWT1kgqKZ2hzwfZIcWnCcbc9n400HU67P_TUsK4Io2oVEEv_0O3cUqhLLenGBGUFq9m6OpAbXQHrQ8uFqtMKQu9NzGAKxa2C1lsb2pK6jLw5TBgUhzHBK4dku91emlJ1e6ja9-jK_DF2w7Tugf7F_0TFP0FlryUCg</recordid><startdate>20241121</startdate><enddate>20241121</enddate><creator>Peng, Chucai</creator><creator>Huang, Jingnan</creator><creator>Sun, Xichen</creator><creator>Nan, Yifei</creator><creator>Chen, Yaohui</creator><creator>Chen, Kun</creator><creator>Feng, Jun</creator><general>MDPI AG</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope></search><sort><creationdate>20241121</creationdate><title>Projectile Penetration into Calcareous Sand Subgrade Airport Runway Pavement with Genetic Algorithm Optimization</title><author>Peng, Chucai ; 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Aiming at the damage assessment of an island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and the calcareous sand subgrade multi-layer structure. First, the Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features, like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, the subsequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete, as well as subgrade calcareous sand. Prior to the multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error ≤ 5.6% providing a reliable numerical tool for deep penetration damage assessments. Third, projectile penetration into the airport runway structure with concrete pavement and calcareous sand subgrade was evaluated with validated DEM model. Penetration numerical simulations with various projectile weight, pavement concrete thickness as well as striking velocity, were performed to achieve the DOP. Moreover, the back-propagation (BP) neural network proxy model was constructed to predict the airport runway penetration data with good agreement realizing rapid and robust DOP forecasting. Finally, the genetic algorithm was coupled with the proxy model to realize intelligent optimization of pavement penetration, whereby the critical velocity projectile just perforates concrete pavement indicating the severest subsequent munition explosion damage.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>39685131</pmid><doi>10.3390/ma17235696</doi><oa>free_for_read</oa></addata></record> |
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subjects | Airports Algorithms Asphalt pavements Back propagation networks Cluster bombs Computer simulation Concrete Concrete pavements Concrete slabs Critical velocity Damage assessment Deformation Discrete element method Error analysis Experiments Explosions Genetic algorithms Granular materials Islands Military supplies Multilayers Numerical analysis Optimization Pavement construction Penetration depth Projectiles Reinforced concrete Rigid pavements Runways Sand Simulation Simulation methods Strain hardening Subgrades Terminal ballistics Thickness Threat evaluation |
title | Projectile Penetration into Calcareous Sand Subgrade Airport Runway Pavement with Genetic Algorithm Optimization |
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