Development and Applications of Artificial Neural Network for Prediction of Ultimate Bearing Capacity of Soil and Compressive Strength of Concrete

Artificial Neural Networks (ANNs) have recently been widely used to model some of the human activities in many areas of science and engineering. One of the distinct characteristics of the ANNs is its ability to learn from experience and examples and then to adapt with changing situations. ANNs does not need a specific equation form that differs from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. This research work focuses on development and application of artificial neural networks in some specific civil engineering problems such as prediction of ultimate bearing capacity of soil and compressive strength of concrete after 28 days. One of the main objectives of this study was the development and application of an ANN for predicting of the ultimate bearing capacity of soil. Hence, a large training set of actual ultimate bearing capacity of soil cases was used to train the network. A neural network model was developed using 1660 data set of nine inputs including the width of foundation, friction angle in three layer, cohesion of three layers and depth of first and second layer are selected as inputs for predicting of ultimate bearing capacity in soil. The model contained a training data set of 1180 cases, a verification data set of 240 cases and a testing data set of 240 cases. The training was terminated when the average training error reached 0.002. Many combinations of layers, number of neurons, activation functions, different values for learning rate and momentum were considered and the results were validated using an independent validation data set. Finally 9-15-1 is chosen as the architecture of neural network in this study. That means 9 inputs with a set of 15 neurons in hidden layer has the most reasonable agreement architecture. This architecture gave high accuracy and reasonable Mean Square Error (MSE). The network computes the mean squared error between the actual and predicted values for output over all patterns. Calculation of mean percentage relative error for training set data, show that artificial neural network predicted ultimate bearing capacity with error of 14.83%. The results prove that the artificial neural network can work sufficiently for predicting of ultimate bearing capacity as an expert system. It was observed that overall construction-related parameters played a role in affecting ultimate bearing capacity, but especially the parameter “friction angle” play a most important role. An important observation is that influencing of the parameter “cohesion” is too less than another parameters for calculating of ultimate bearing capacity of soil. Also in this thesis is aimed at demonstrating the possibilities of adapting artificial neural Also in this thesis is aimed at demonstrating the possibilities of adapting artificial neural networks (ANN) to predict the compressive strength of concrete. To predict the compressive strength of concrete the six input parameters, such as, cement, water, silica fume, superplasticizer, fine aggregate and coarse aggregate identified. Total of 639 different data sets of concrete were collected from the technical literature. Training data sets comprises 400 data entries, and the remaining data entries (239) are divided between the validation and testing sets. The training was stopped when the average training error reached 0.007. A detailed study was carried out, considering two hidden layers for the architecture of neural network. The performance of the 6-12-6-1 architecture was the best possible architecture. The MSE for the training set was 5.33% for the 400 training data points, 6.13% for the 100 verification data points and 6.02 % for the 139 testing data points. It can recognize the concrete in term of ‘strength’ with a confidence level of about 95%, which is considered as satisfactory from an engineering point of view. It was found from sensitivity analyses performed on a neural network model that the cement has the maximum impact on the compressive strength of concrete. Finally, the results of the present investigation were very encouraging and indicate that ANNs have strong potential as a feasible tool for predicting the ultimate bearing capacity of soil and compressive strength of concrete

Assessment of Urban Noise in School Environments - Case Study in Batu Pahat, Johor

In recent decades, urban noise has become well-known as one of the critical problems affecting the quality of life in urban areas around the world. Noise assessment is becoming more common in Malaysia as many cities in this country become highly populated and industrialized with serious noise pollution issues, particularly in schools. The aims of this study are to identify the factors and effects of noise in the school environments and evaluate the acoustic comfort of the teachers during the teaching lesson in the school environment. A total of 3 schools located in the urban area of Batu Pahat, Johor, which were Sekolah Menengah Kebangsaan (SMK), Tinggi Batu Pahat, SMK Tun Aminah and SMK Semerah were chosen as study areas. In the present study, questionnaires survey using Google Form were distributed to teachers working in the selected schools. Expert reviews and a pilot study were carried out before the actual survey. The results indicated that the noise factors were coming from inside and outside the classroom, mainly from traffic noise and heavy vehicles near the school environment and noise from student activities and chattering. Teachers need to raise their voice during the teaching and learning process, and some of them had a sore throat.  SMK Tinggi Batu Pahat and SMK Tun Aminah teachers were uncomfortable with the existing acoustic comfort of the school environments

Assessment of Urban Noise in School Environments - Case Study in Batu Pahat, Johor

In recent decades, urban noise has become well-known as one of the critical problems affecting the quality of life in urban areas around the world. Noise assessment is becoming more common in Malaysia as many cities in this country become highly populated and industrialized with serious noise pollution issues, particularly in schools. The aims of this study are to identify the factors and effects of noise in the school environments and evaluate the acoustic comfort of the teachers during the teaching lesson in the school environment. A total of 3 schools located in the urban area of Batu Pahat, Johor, which were Sekolah Menengah Kebangsaan (SMK), Tinggi Batu Pahat, SMK Tun Aminah and SMK Semerah were chosen as study areas. In the present study, questionnaires survey using Google Form were distributed to teachers working in the selected schools. Expert reviews and a pilot study were carried out before the actual survey. The results indicated that the noise factors were coming from inside and outside the classroom, mainly from traffic noise and heavy vehicles near the school environment and noise from student activities and chattering. Teachers need to raise their voice during the teaching and learning process, and some of them had a sore throat.  SMK Tinggi Batu Pahat and SMK Tun Aminah teachers were uncomfortable with the existing acoustic comfort of the school environments

Dynamic Behavior of Connected Structures

The effect of connected structure on SMK Bukit Tinggi building was investigated based on their dynamic behaviour. The structure was constructed with four main buildings are interconnected. Ambient vibration testing (AVT) was used to predict the dynamic response and characteristics by using triaxial 1Hz seismometer and CityShark data logger. The AVT signals were analysed using GEOPSY software for Fourier Amplitude Spectral (FAS). All predominant frequencies (fo) and mode shapes were identified and verified with previous research finding using similar testing approach on the same building, but with different instrument of accelerometer sensor and ARTeMIS software processing tool. Five modes of fo were found from the FAS curves from this research, but only four fo were obtained from ARTeMIS analysis. The highest deviation percentage was indicated at the 5th mode of building frequency at 9.5 %, but 0 to 2.5% to the rest frequencies mode (1st to 4th frequencies mode). Ununiformed buildings response behaviour was believed to contribute, and initiate active progressive shear cracking on the slab panels, columns, beam and at the reentrant corners between laboratory and academic buildings of Building C. It worst when the buildings were struck by several earthquakes’ series. The unsynchronised oscillation between adjacent buildings to Building C had induced couple lateral and torsional deformations. The structural and non-structural damages mostly concentrated at the reentrant corners and mid-span of the building, which identical to the location of maximum deflection amplitudes. In conclusion, strict attention must be emphasized on the fo and mode shapes based on their dynamic response and characteristics analyses, which could be altered by the presence of connected structures

Dynamic Behavior of Connected Structures

The effect of connected structure on SMK Bukit Tinggi building was investigated based on their dynamic behaviour. The structure was constructed with four main buildings are interconnected. Ambient vibration testing (AVT) was used to predict the dynamic response and characteristics by using triaxial 1Hz seismometer and CityShark data logger. The AVT signals were analysed using GEOPSY software for Fourier Amplitude Spectral (FAS). All predominant frequencies (fo) and mode shapes were identified and verified with previous research finding using similar testing approach on the same building, but with different instrument of accelerometer sensor and ARTeMIS software processing tool. Five modes of fo were found from the FAS curves from this research, but only four fo were obtained from ARTeMIS analysis. The highest deviation percentage was indicated at the 5th mode of building frequency at 9.5 %, but 0 to 2.5% to the rest frequencies mode (1st to 4th frequencies mode). Ununiformed buildings response behaviour was believed to contribute, and initiate active progressive shear cracking on the slab panels, columns, beam and at the reentrant corners between laboratory and academic buildings of Building C. It worst when the buildings were struck by several earthquakes’ series. The unsynchronised oscillation between adjacent buildings to Building C had induced couple lateral and torsional deformations. The structural and non-structural damages mostly concentrated at the reentrant corners and mid-span of the building, which identical to the location of maximum deflection amplitudes. In conclusion, strict attention must be emphasized on the fo and mode shapes based on their dynamic response and characteristics analyses, which could be altered by the presence of connected structures

Application of artificial neural networks to predict compressive strength of high strength concrete

A method to predict 28-day compressive strength of high strength concrete (HSC) by using MFNNs is proposed in this paper. The artificial neural networks (ANN) model is constructed trained and tested using the available data. A total of 368 different data of HSC mix-designs were collected from technical literature. The data used to predict the compressive strength with ANN consisted of eight input parameters which include cement, water, coarse aggregate, fine aggregate, silica fume, superplasticizer, fly ash and granulated grated blast furnace slag. For the training phase, different combinations of layers, number of neurons, learning rate, momentum and activation functions were considered. The training was terminated when the root mean square error (RMSE) reached or was less than 0.001 and the results were tested with test data set. A total of 30 architectures were studied and the 8-10-6-1 architecture was the best possible architecture. The results show that the relative percentage error (RPE) for the training set was 7.02% and the testing set was 12.64%. The ANNs models give high prediction accuracy, and the research results demonstrate that using ANNs to predict concrete strength is practical and beneficial

Artificial neural networks and adaptive neuro-fuzzy inference systems for structural damage identification using vibration data / Seyed Jamalaldin Seyed Hakim

The main objective of this study is to develop and demonstrate the potential of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) based damage identification techniques for damage localization and severity prediction in I-beam and steel girder bridge structures using modal properties. In this research, experimental modal analysis and numerical simulations of these structures were carried out to generate dynamic parameters of structures and also to investigate the applicability of ANNs and ANFIS for improved structural damage identification. Vibration data from a scaled down steel girder bridge deck and nine I-beams structures with regard to different damage scenarios for each structure were measured to obtain the first five natural frequencies and mode shapes of the structures. Single and multiple damage cases which include double, triple and quad damages were induced in I-beam structures, while only single damage was inflicted in the girder bridge at different locations. Also, numerical modeling of these structures and computation of the responses were carried out using commercial software. In this research, a combination of natural frequencies and mode shapes for the first five modes of these structures were selected as the input parameters for damage identification purpose. In damage identification using ANNs, five individual networks corresponding to mode 1 to mode 5 were trained, and then a method based on neural network ensemble was proposed to combine the outcomes of the individual neural networks to a single network. Based on this study, ANNs were able to detect the severity and location in single and multiple damages accurately. Some insignificant errors for numerical datasets due to modeling errors of the structure and some less accurate results due to the existence of node points in the structure were demonstrated. The ensemble network produces better damage identification outcomes than the individual networks and shows high accuracy of damage identification predictions. Besides that, results show the ANFIS model could identify the severity and location of damage in I-beam and girder bridge structures with high level of accuracy and demonstrated that the outcomes of ANFIS were very close to targets and the developed ANFIS model can be applied as a very strong tool for identification of damage. By incorporating ANNs and ANFIS techniques, the potential and accuracy of damage identification can be improved and some significant major problems of conventional methods can be overcome. According to the results of the comparative study, although both ANNs and ANFIS presented good predictions, ANNs were very sensitive to insufficient and noisy datasets as compared to ANFIS. However, ANFIS provided a structure for the combination of fuzzy logic and ANNs and was less sensitive to insufficient and noisy data and showed more flexible technique than ANN. The comparative study showed that, although in some cases both techniques demonstrated high level of predictions, the ANFIS showed a superior capability to damage predictions using vibration datasets of structures. In conclusion, the ANFIS technique outperformed the ANN and demonstrated the best performance with lowest AE and highest correlation coefficient