Comparing Results of Building Settlement due to Asymmetric Settlement by PLAXIS and Artificial Neural Networks(Case Study: Residential Buildings in Tehran)

سال انتشار: 1395
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 338

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شناسه ملی سند علمی:

ICASCE03_005

تاریخ نمایه سازی: 6 اسفند 1395

چکیده مقاله:

Recently, the lack of concern for technical and constructional considerations of construction projects has caused many post-construction problems. In this study, a residential building in Tehran City (in Tehran Province) was studied, and some of the most important problems of this building were emergence of cracks on the structure, asymmetrical settlement (caused by presence of layers of fill material over the natural bed with different thickness), and lack of suitable compaction beneath the foundation at the time of construction. In this research, to determine depth of the fill material layer, eight pits were excavated beneath the project foundation and the required parameters were obtained through in-situ testing (such as in-situ shear test, plate loading test, etc.). Considering the investigation results, to improvement and stabilization the residential building under study, post-injection methods with piles, micro-piles and retaining walls were used as methods of improvement based on the confinement technique. The following, stability analysis of the building was performed in Plaxis and afterwards, to assess validity of results a network of different data types was trained in different conditions and was tested within the research scope. Finally, the results were compared to Plaxis results and given the compliance of data with the neural network, the correlation coefficient between the predicted and actual values of settlement is 39.3. at the end, based on technical, implementation, and economic aspects, the use of micro-piles and retaining walls (as confinement elements) was selected and employed as the best method.

کلیدواژه ها:

Soil improvement ، Asymmetrical settlement ، Fill material ، Plaxis and Artificial Neural Networks

نویسندگان

Esmail Aflaki

Professor, Department of civil engineering , Amirkabir University of Technology

Mohammad Arabameri

Ph.D Geotechnical Student, Amirkabir University of Technology

Mehdi Arabameri

Master’s Degree in the field of Geotechnical Engineering, Executive Deputy at Shaloodeh Khak Consulting Engineers Company

Somaye Akbarnezhad

Master’s Degree in the field of Geotechnical Engineering, Technical experts and Soil improvement at Shaloodeh Khak Consulting Engineers Company