1,720,979 research outputs found
Program usahasama perkongsian ilmu antara MINDA, Politeknik Ungku Omar dan Fakulti Kejuruteraan Awam, UiTM Pulau Pinang / Ts. Syahrul Fithry Senin
No full textSatu program webinar telah diadakan di antara Fakulti Kejuruteraan Awam, UiTM Cawangan Pulau Pinang, Politeknik Ungku Omar (PuO) dan MINDA pada 18 Mac 2021. Wakil dari Fakulti Kejuruteraan Awam UiTM Cawangan Pulau Pinang, Ts. Syahrul Fithry Bin Senin telah dijemput menjadi salah seorang ahli panel di dalam kerjasama ini untuk perkongsian ilmu mengenai penyenggaraan bangunan menggunakan teknologi industri 4.0. Program ini telah dihadiri oleh hampir 50 orang pelajar dari Jabatan Kejuruteraan Awam, PuO. Ketua Jabatan Kejuruteraan Awam PuO iaitu Dr. Azuin Binti Ramli telah mengalu-alukan kehadiran panel di dalam webinar tersebut
Majlis menandatangani Memorandum Persefahaman (MoU) antara UiTM Cawangan Pulau Pinang, Malaysia dengan UniMal, Aceh, Indonesia / Ts. Syahrul Fithry Senin
Full text linkPusat Pengajian Kejuruteraan Awam (PPKA), Universiti Teknologi MARA Cawangan Pulau Pinang telah menandatangani Memorandum Persefahaman (MoU) bersama Universiti Malikussaleh Aceh (UniMal), Indonesia pada 22 hb September 2021 bagi menjalinkan persefahaman mengenai pembangunan penyelidikan akademik dan pemikiran saintifik, pertukaran staf, kerjasama dan rakan kongsi antarabangsa. Majlis ini ditandatangani oleh Yang Berusaha, Prof. Ts. Dr. Salmiah Kasolang, Rektor UiTM Pulau Pinang dan juga Prof. Dr. Ir. Herman Fithra, Rektor UniMal. Majlis berlangsung secara atas talian dengan menggunakan platform Google Meet bermula pada jam 3 petang. Peneraju MoU ini, iaitu Ts. Syahrul Fithry Bin Senin telah berusaha keras selama beberapa bulan bersama-sama dengan Ketua Pusat Pengajian PPKA, iaitu Dr, Anas Ibrahim, bermesyuarat untuk menjayakan program ini
Collaborative monthly technical webinar between Civil Engineering Studies, UiTM Penang Branch and Faculty of Civil Engineering and Technology, Universiti Malaysia Perlis
No full textOn December 31, 2024, the Faculty of Civil Engineering and Technology at Universiti Malaysia Perlis(UniMAP) hosted an informative two-hour (10 am until 12 noon) technical monthly webinar titled "Ground Penetrating Radar(GPR) Application for Reinforced Concrete Structures Construction Quality Control". The session was presented by the invited speaker Ts. Syahrul Fithry Bin Senin from the Civil Engineering Studies, Universiti Teknologi MARA Penang Branch via online platform. The webinar aimed to educate participants on non- destructive assessment techniques for reinforced concrete structures quality control using GPR technology. The participants were awarded 2CPD training hours from Board of Engineers Malaysia,and e-certificate from the webinar organizer. The webinar began with an introduction to internal concrete deterioration and defects,drawing an analogy between human health assessment and structural health monitoring. The speaker emphasized that just as medical professionals use various diagnostic tools to assess human health, civil engineers need reliable methods to evaluate the"health" of reinforced concrete structures. Common quality parameters requiring inspection in RC structures include:
• Concrete cover
• Rebar diameter
• Rebar spacing
• Quantity of rebar sand their arrangement
• Embedded "anomalies" or materials within RC structures
The importance of these inspections was highlightedthrough several key reasons:
• Preventivemaintenance of RC structures
• Avoiding excessive costlystructuralrepairworks
• Evaluatingstructuralintegrityandsafety
• Creating accurate maps pfor clients to ensure avoidance during drilling operation
International Technical Lectures Colloboration (IILC) session between Civil Engineering Studies, Universiti Teknologi MARA Cawangan Pulau Pinang, Malaysia and The Department of Civil Engineering, Universitas Malikussaleh, Indonesia / Syahrul Fithry Senin, Khairullah Yusuf and Maizuar Maizuar
Full text linkThis article elucidates an international academic collaboration between the Civil Engineering Studies at the College of Engineering, Universiti Teknologi MARA Cawangan Pulau Pinang (UiTM), Malaysia, and its memorandum of understanding (MOU) partner, the Department of Civil Engineering, Universitas Malikussaleh, Aceh (UNIMAL), Indonesia. This endeavour was undertaken as a component of a series of academic lecture sessions between the two institutions over the course of 2023
Effects of perforations on load carrying mechanism of perforated hollow sections inspired by nature / Woo Yian Peen and Syahrul Fithry Senin
Full text linkSustainability in built environment has attracted more attention, effort to increase efficiency of materials and elements used in construction becomes more important. Structural members in construction should be made more efficient in terms of the ratio of load carrying capacity to self-weight without compromising the safety of the structure as a whole. In this respect, nature offers a lot of idea to be copied and explored. Inspired by the Cholla cactus skeleton, this study investigates how the load carrying capacity of hollow structural members affected by perforations. Computational analysis using finite element method has been carried out for a total 12 models with different perforation patterns in order to investigate the strength and stiffness of models under compressive, flexural and torsional load cases. The results of perforated hollow sections have been compared with the control model without perforation. It is found that load carrying capacity not only influenced by change in section properties, but also by the perforations arrangement patterns and the load path before and after the loads being interrupted by the perforations where the spiral arrangement pattern has significant influence in certain load cases. Findings from this study is essential in leading to proposal of efficient structural members
Prediction of shear strength of concrete using the artificial neural network / R. Rohim, S.F. Senin and N.F. Azman
Full text linkArtificial neural networks (ANN) are known to be increasingly popular and used in several engineering applications, such as in the civil engineering field. In this study, this method was used to develop an optimal model to predict the shear strength of concrete using the experimental data sets. All the data sets were trained and tested using ANN to obtain the prediction of the shear strength of concrete material. The model ANN was trained and tested using test data sets obtained from 51 concrete mixes from previous experimental data sets. 33 (65%) concrete mixes data sets were chosen randomly and used as input for training. The remaining 18 (35%) mixes data were divided equally into testing and validation data sets. Feed-forward backpropagation was chosen for the neural network design and LevenbergMarquardt was used as the learning algorithm. An S-shaped sigmoid function was used to predict the probability as output between the range 0 to 1. Ten different types of architecture networks with different types of structures and neurons number were used to obtain the best model. The optimal ANN architecture (33-10-1) was found to have the highest correlation coefficient (R) of 0.99888 and the lowest mean square error (MSE) 0.00085. The shear strength based on the ANN model perfectly matched the values of the experimental data sets
International Technical Lectures Collaboration (IILC) session between Civil Engineering Studies, Universiti Teknologi Pulau Pinang Branch, Malaysia and The Department of Civil Engineering, Universitas Malikussaleh, Indonesia / Syahrul Fithry Senin, Khairullah Yusuf and Maizuar Maizuar
Full text linkAbstract: This article elucidates an international academic collaboration between the Civil Engineering Studies at the College of Engineering, Universiti Teknologi MARA (UiTM) Pulau Pinang Branch, Malaysia, and its Memorandum of Understanding (MoU) partner, the Department of Civil Engineering, Universitas Malikussaleh (UNIMAL), Aceh, Indonesia. This endeavour was undertaken as part of a series of academic lecture sessions between the two institutions throughout2023
A study of laminated composite materials using aclap computer program / Syahrul Fithry Senin and Ayurahani Che Lah
No full textThis study is focused on the analysis of the composite materials by using the developed computer program, Automatic Composite Laminated Analysis Program (ACLAP), in FORTRAN language. The purposes of this study are to determine the capability and the accuracy of the program for solving the analysis that related with the laminate materials. This study also can provide an understanding of the underlying principles and techniques associated with the stress analysis and strength predictions of composite material structures.There are six examples that are going to be analyzing by using the
FORTRAN program, each of the examples has their own problem statement. The FORTRAN results from the examples will be compared with the theory calculation. At the end of this study a user friendly computer program is produced with the intention to assist the lecturer for teaching or learning purpose
Modeling of bolt behavior using finite element / Syahrul Fithry Senin and Jumatirah Mohd Alias
Full text linkThe failure of bolted connections will present serious economic and human consequences, so it is important to obtain a better understanding of the structural behavior of bolted connections. The objective of this study is to find the best numerical bolt model and analyzing their behavior when subjected to tensile loads. There are three models of bolt will be analyze for this study. First model was analysed using brick element, second model using beam element
and the third model bolt using joint element. For this study the finite element software, LUSAS will be used to study various connection models. Convergence study was conducted in this research. For this study, it was found that the brick element is the best model as compared with the beam element and joint element
Prediction of Total Maximum Daily Loads (TMDLs) of pollutants in river by using artificial neural network (ANN) / Khairunnisa Khairudin, Mohamed Syazwan Osman and Syahrul Fithry Senin
Full text linkTotal Maximum Daily Load (TMDL) studies are crucial in determining a pollutant reduction target and allocates load reductions necessary to the source(s) of the pollutant. Existing modelling approaches to simulate TMDL allocations of point source and non-point source pollutants typically consist of linking watershed model, receiving water transport model, and receiving water quality model. Such deterministic model requires extensive data of the underlying process compared to artificial neural network (ANN) that simulates data based on data-driven method. In this study, biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), and ammoniacal nitrogen (NH3-N) loads for Muda River is predicted using ANN. The model is developed based on historical monthly concentration data and discharge data from 2013 to 2018 provided by Department of Environment (DOE), Malaysia. These parameters were introduced as inputs, whereas TMDL as outputs of the threelayer feed-forward back-propagation ANN. The learning algorithm used is Bayesian Regularization with tansig transfer function at the hidden layer and purelin transfer function at the output layer. Here, the number of neurons tested to obtain the optimum number of hidden layer nodes is 5, 7, 9, 11, and 13, which run at different epochs: 1000, 2000, and 3000. Model performance was evaluated using mean absolute percent error (MAPE), coefficient of determination (R2), root mean square error (RMSE), and model efficiency (E). The best model for TMDL of BOD is 6:13:1 at epoch 2000 with 0.0004% (MAPE), 1.0 (R2), 0.0005 (RMSE), and 1.0 (E). Meanwhile, the best model for TMDL of COD is 6:5:1 at epoch 3000 with 0.00004% (MAPE), 1.0 (R2), 0.0004 (RMSE), and 1.0 (E). Furthermore, the best model for TMDL of SS is 6:5:1 at epoch 3000 with 0.0038% (MAPE), 0.99 (R2), 0.1 (RMSE) and 1.0 (E). Finally, the best model for TMDL of NH3-N is 6:5:1 at epoch number 3000 with 0.0001% (MAPE), 1.0 (R2), 9.47x10-6 (RMSE) and 1.0 (E). It can be concluded that ANN is an excellent modelling approach to substitute deterministic models for TMDL prediction
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