000004881 001__ 4881
000004881 005__ 20141119144601.0
000004881 04107 $$aeng
000004881 046__ $$k2002-06-02
000004881 100__ $$aCeylan, Halil
000004881 24500 $$aARTIFICIAL NEURAL NETWORKS FOR THE ANALYSIS OF SLABS UNDER SIMULTANEOUS AIRCRAFT AND TEMPERATURE LOADING

000004881 24630 $$n15.$$pProceedings of the 15th ASCE Engineering Mechanics Division Conference
000004881 260__ $$bColumbia University in the City of New York
000004881 506__ $$arestricted
000004881 520__ $$2eng$$aThis study focuses on the development and performance of a comprehensive artificial neural network (ANN) model for the analysis of jointed concrete slabs under simultaneous aircraft and temperature loading. Using the results from the ILLI-SLAB finite element program, a comprehensive artificial neural network model was trained for the different loading conditions of gear load only, temperature load only, and simultaneous aircraft and temperature loading cases. Special consideration has been given to the loading of a typical jointed slab assembly under the tri-tandem type gear of the Boeing 777 aircraft. In addition to various slab load locations (interior, corners, and two mid-span slab edges) and joint load transfer efficiencies, a wide range of realistic airfield slab thicknesses and subgrade supports have been considered as ANN input conditions. Comparing the ANN predictions to the ILLI-SLAB solutions validated the performance of the ANN model. The trained ANN model gave maximum bending stresses and maximum vertical deflections within an average error of 1.4 percent of those obtained directly from ILLI-SLAB analyses. It is also shown that the principle of superposition is not valid for simply adding the critical pavement responses (slab deflections and bending stresses) obtained under gear loading only and temperature loading only cases. The summation does not usually produce the same pavement response as obtained from the simultaneous gear and temperature loading. The typical ANN prediction time is about 0.3 million times faster than the average ILLI-SLAB finite element solution. The use of an ANN-based design tool is deemed to be very effective for studying hundreds or thousands of “what if” scenarios for including the temperature effects in pavement analysis and design.

000004881 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000004881 653__ $$aArtificial Neural Networks, Boeing 777 Aircraft, Jointed Concrete Slabs, Rigid Pavement Analysis and Design, Finite Element Analysis, Airfield Pavements, Temperature Loading

000004881 7112_ $$a15th ASCE Engineering Mechanics Division Conference$$cNew York (US)$$d2002-06-02 / 2002-06-05$$gEM2002
000004881 720__ $$aCeylan, Halil$$iTutumluer, Erol$$iBarenberg, Ernest J.$$iLife
000004881 8560_ $$ffischerc@itam.cas.cz
000004881 8564_ $$s2726534$$uhttps://invenio.itam.cas.cz/record/4881/files/484.pdf$$yOriginal version of the author's contribution as presented on CD, .
000004881 962__ $$r4594
000004881 980__ $$aPAPER