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<rss version="2.0"><channel><title>Scientia Research Library</title><link>http://www.scientiaresearchlibrary.com</link><description>Scientia Research Library make easy to publish research articles or research papers, which is a great opportunity for everyone to fulfill their requirements. Different varieties of journals related to science and technology which are scientifically same can be published here. The Scientia Research  Library  is having an  open - access and peer review policy  to permit  and  understand  use with  required  acceptance  of   the  original . Our   aim is to provide researchers from various diverse fields like engineering, applied chemistry, applied science and research etc., a unique way to give light to their findings.</description><article><ArtTitle>
	Artificial Neural Networks for Bridge Deterioration Age Estimation
</ArtTitle><PubName>Scientia Research Library</PubName><JournalName>Journal of Engineering And Technology Research</JournalName><EISSN/><year>2024</year><volume>12</volume><issue>4</issue><AuthorName>
	Charles

	pages:1-7
</AuthorName><PageNo>1</PageNo><Abstract>
	Because it is challenging to estimate or predict the service life of reinforced concrete bridges, deterioration of these structures is a significant structural engineering concern. To estimate the service life, two different models were created: deterministic and probabilistic. These models dependability is called into doubt since they fail to take into consideration the numerous variables at play. Therefore, based on real deterioration data, an artificial neural network (ANN) is employed in this study to estimate the age of RC bridge deterioration. ANN is trained and tested using historical records of London bridges. The purpose of a feedforward neural network is to determine the age of degeneration. Bridge type, member type, exposure, and flaws are ANN inputs, and the age of the faults is the target. To choose and track the most crucial parameters that could impact ANN performance, an experiment is designed. Given the kind of data used in neural network training, the outcomes were unsatisfactory.

	Keywords: Bridge deterioration, artificial neural networks, design of experiment
</Abstract><URLs><abstract>http://www.scientiaresearchlibrary.com/archive-abs.php?arc=930</abstract><Fulltext><pdf>http://www.scientiaresearchlibrary.com/archive/Artificial Neural Networks for Bridge Deterioration Age Estimation.pdf</pdf></Fulltext></URLs></article><article><ArtTitle>
	Curved Continuous Rigid Frame Bridges with Initial High Pier Imperfections at the Largest Cantilever Stage: Geometrically Nonlinear Stability
</ArtTitle><PubName>Scientia Research Library</PubName><JournalName>Journal of Engineering And Technology Research</JournalName><EISSN/><year>2024</year><volume>12</volume><issue>4</issue><AuthorName>
	Chatuluka, Quasshie

	pages:1-13
</AuthorName><PageNo>1</PageNo><Abstract>
	We examined the geometrically nonlinear stability (GNS) of curved continuous rigid frame bridges (CRFB) at the biggest cantilever stage when the pier flaws were initially substantial. For models in an ideal state, initial tilt state, initial bend state, initial material imperfection state, or with various pier types, the stability safety factors were calculated and compared. When geometrically nonlinear influences are taken into account, the stability safety factors diminish. This decrease is more pronounced when the original material flaw manifests in the middle and lower sections of the pier. Stability is further reduced by a cross section in the double-limb piers. In fact, it is essential to minimize initial bend in order to guarantee a safe structure. Both start tilt and initial bend significantly affect stability, particularly the latter. Whether a single-limb or double-limb pier is used, these elements have an almost equal impact on stability. All of these findings point to the need for GNS analysis in order to properly evaluate the safety of a curved CRFB with high piers.

	Keywords: Curved continuous rigid frame bridge (CRFB), high pier, geometrically nonlinear analysis (GNS), stability safety factor, finite element (FE) method, initial imperfections.
</Abstract><URLs><abstract>http://www.scientiaresearchlibrary.com/archive-abs.php?arc=931</abstract><Fulltext><pdf>http://www.scientiaresearchlibrary.com/archive/When FRP is applied to the S hear R reinforcing B ridges.pdf</pdf></Fulltext></URLs></article><article><ArtTitle>
	Curved Continuous Rigid Frame Bridges with Initial High Pier Imperfections at the Largest Cantilever Stage: Geometrically Nonlinear Stability
</ArtTitle><PubName>Scientia Research Library</PubName><JournalName>Journal of Engineering And Technology Research</JournalName><EISSN/><year>2024</year><volume>12</volume><issue>4</issue><AuthorName>
	Chatuluka, Quasshie

	pages:1-13
</AuthorName><PageNo>1</PageNo><Abstract>
	We examined the geometrically nonlinear stability (GNS) of curved continuous rigid frame bridges (CRFB) at the biggest cantilever stage when the pier flaws were initially substantial. For models in an ideal state, initial tilt state, initial bend state, initial material imperfection state, or with various pier types, the stability safety factors were calculated and compared. When geometrically nonlinear influences are taken into account, the stability safety factors diminish. This decrease is more pronounced when the original material flaw manifests in the middle and lower sections of the pier. Stability is further reduced by a cross section in the double-limb piers. In fact, it is essential to minimize initial bend in order to guarantee a safe structure. Both start tilt and initial bend significantly affect stability, particularly the latter. Whether a single-limb or double-limb pier is used, these elements have an almost equal impact on stability. All of these findings point to the need for GNS analysis in order to properly evaluate the safety of a curved CRFB with high piers.

	Keywords: Curved continuous rigid frame bridge (CRFB), high pier, geometrically nonlinear analysis (GNS), stability safety factor, finite element (FE) method, initial imperfections.
</Abstract><URLs><abstract>http://www.scientiaresearchlibrary.com/archive-abs.php?arc=932</abstract><Fulltext><pdf>http://www.scientiaresearchlibrary.com/archive/When FRP is applied to the S hear R reinforcing B ridges.pdf</pdf></Fulltext></URLs></article></channel></rss>
