MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03840nam a2200205 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
240521s2023 |||||||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| ISSN-L |
phd |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
620.1360285378242 |
| Item number |
FAT |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Relator term |
author |
| Personal name |
Fatima Khalid |
| 9 (RLIN) |
882277 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Artificial Intelligence Based Constitutive Modelling of Recycled Aggregate Concrete (PhD Thesis) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Karachi : |
| Name of publisher, distributor, etc. |
NED university of Engineering and Technology Department of Civil Engineering, |
| Date of publication, distribution, etc. |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 138 p. : |
| Other physical details |
ill |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes Bibliographical References |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
ABSTRACT <br/><br/>The rapid global urbanisation has led to increased construction activity, resulting in a high demand for raw materials and subsequent environmental degradation. To address this issue, recycled aggregate concrete (RAC) has emerged as a viable solution by utilising concrete waste from demolished structures. However, the incorporation of RAC into structural applications requires a better understanding of the behaviour of RAC under multiaxial states of stress. This can be achieved by developing a constitutive model that can simulate its behaviour under various stress conditions. This study, therefore, focuses on developing a constitutive model for RAC within the framework of damage mechanics, using artificial intelligence (AI) techniques to estimate the model parameters. This study modifies existing elasto-damage natural aggregate concrete (NAC) model proposed in literature by Khan and Zahra to incorporate the effect of replacing natural coarse aggregates with recycled coarse aggregates. The proposed constitutive model incorporates essential features of concrete, such as different behaviour in tension and compression, stiffness degradation, strain softening, strength gain under confinement, and volumetric dilatation. Also the proposed model considers incorporation of plastic strains along with damage. Four parameters of the model i.e. compressive strength (f'c), Elastic Modulus (E), and calibrated parameters that control the phenomenoologically known peak strengths to model the different behaviour of concrete in tension and compression ( a and B), were modified and predicted using artificial neural network (ANN). Compressive strength and elastic modulus were defined as a function of different parameters (water to cement ratio, amount of cement, water, natural and recycled coarse aggregate, fine aggregate, fly ash and superplasticizers contents, and the replacement ratio of recycled aggregate concrete). a and /3 are defined as functions of concrete compressive strength, elastic modulus, and stress paths. Experimental investigations were conducted at different percentages of recycled coarse aggregate replacement i.e. 0%, 30%, 50%, 70% and 100% at three different water to cement ratio of 0.4, 0.5 and 0.6 to determine mechanical properties and develop experimental stress-strain curves. Experimental results along with existing published data were used to predict the aforementioned parameters. The constitutive model was validated with the existing experimental data. The resulting constitutive model was designed to be easily integrated into finite element codes. Furthermore, experiments were conducted reflecting multiaxial state of stress conditions. These were simulated in nonlinear FEM software (ATENA-GiD) after incorporating proposed constitutive model in the software. The results showed that the proposed model can predict peak stress, initial stiffness, post peak behaviour in a good manner and overall results were found to be in a close agreement with the experimental and published results. <br/><br/> |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
881968 |
| Topical term or geographic name entry element |
Aggregates Building Materials Recycling Thesis |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| 9 (RLIN) |
881969 |
| Topical term or geographic name entry element |
Concrete Recycling Artificial Intelligence Thesis |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/98702.pdf">https://eaklibrary.neduet.edu.pk:8443/catalog/bk/books/toc/98702.pdf</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Suppress in OPAC |
No |
| Koha item type |
PHD Thesis |
