4,897 research outputs found
Causative factors of construction and demolition waste generation in Iraq Construction Industry
The construction industry has hurt the environment from the waste generated during
construction activities. Thus, it calls for serious measures to determine the causative
factors of construction waste generated. There are limited studies on factors causing
construction, and demolition (C&D) waste generation, and these limited studies only
focused on the quantification of construction waste. This study took the opportunity to
identify the causative factors for the C&D waste generation and also to determine the
risk level of each causal factor, and the most important minimization methods to
avoiding generating waste. This study was carried out based on the quantitative
approach. A total of 39 factors that causes construction waste generation that has been
identified from the literature review were considered which were then clustered into 4
groups. Improved questionnaire surveys by 38 construction experts (consultants,
contractors and clients) during the pilot study. The actual survey was conducted with
a total of 380 questionnaires, received with a response rate of 83.3%. Data analysis
was performed using SPSS software. Ranking analysis using the mean score approach
found the five most significant causative factors which are poor site management, poor
planning, lack of experience, rework and poor controlling. The result also indicated
that the majority of the identified factors having a high-risk level, in addition, the better
minimization method is environmental awareness. A structural model was developed
based on the 4 groups of causative factors using the Partial Least Squared-Structural
Equation Modelling (PLS-SEM) technique. It was found that the model fits due to the
goodness of fit (GOF ≥ 0.36= 0.658, substantial). Based on the outcome of this study,
39 factors were relevant to the generation of construction and demolition waste in Iraq.
These groups of factors should be avoided during construction works to reduce the
waste generated. The findings of this study are helpful to authorities and stakeholders
in formulating laws and regulations. Furthermore, it provides opportunities for future
researchers to conduct additional research’s on the factors that contribute to
construction waste generation
Comparative performance of some popular ANN algorithms on benchmark and function approximation problems
We report an inter-comparison of some popular algorithms within the
artificial neural network domain (viz., Local search algorithms, global search
algorithms, higher order algorithms and the hybrid algorithms) by applying them
to the standard benchmarking problems like the IRIS data, XOR/N-Bit parity and
Two Spiral. Apart from giving a brief description of these algorithms, the
results obtained for the above benchmark problems are presented in the paper.
The results suggest that while Levenberg-Marquardt algorithm yields the lowest
RMS error for the N-bit Parity and the Two Spiral problems, Higher Order
Neurons algorithm gives the best results for the IRIS data problem. The best
results for the XOR problem are obtained with the Neuro Fuzzy algorithm. The
above algorithms were also applied for solving several regression problems such
as cos(x) and a few special functions like the Gamma function, the
complimentary Error function and the upper tail cumulative
-distribution function. The results of these regression problems
indicate that, among all the ANN algorithms used in the present study,
Levenberg-Marquardt algorithm yields the best results. Keeping in view the
highly non-linear behaviour and the wide dynamic range of these functions, it
is suggested that these functions can be also considered as standard benchmark
problems for function approximation using artificial neural networks.Comment: 18 pages 5 figures. Accepted in Pramana- Journal of Physic
Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes
Construction over peat area have often posed a challenge to geotechnical engineers.
After decades of study on peat stabilisation techniques, there are still no absolute
formulation or guideline that have been established to handle this issue. Some
researchers have proposed solidification of peat but a few researchers have also
discovered that solidified peat seemed to decrease its strength after a certain period of
time. Therefore, understanding the chemical and biological reaction behind the peat
solidification is vital to understand the limitation of this treatment technique. In this
study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and
Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom
ash at various ratio. The mixtures of peat-binder-filler were subjected to the
unconfined compressive strength (UCS) test, bacterial count test and chemical
elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over
curing period were observed. Mixing 1 samples showed a steadily increase in strength
over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern
at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial
count and enzymatic activity with increase quantity of crystallites. Samples with lower
strength recorded increase in bacterial count and enzymatic activity with less
crystallites. Analysis using XRD showed that pargasite
(NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in
the lower strength samples, pargasite was predicted to be converted into monosodium
phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated
as 303.60. This showed that reaction which happened during solidification work was
inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as
135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount
of enzyme secretes is higher
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Multivariate time series analysis for short-term forecasting of ground level ozone (O3) in Malaysia
The declining of air quality mostly affects the elderly, children, people with asthma,
as well as a restriction on outdoor activities. Therefore, there is an importance to
provide a statistical modelling to forecast the future values of surface layer ozone (O3)
concentration. The objectives of this study are to obtain the best multivariate time
series (MTS) model and develop an online air quality forecasting system for O3
concentration in Malaysia. The implementations of MTS model improve the recent
statistical model on air quality for short-term prediction. Ten air quality monitoring
stations situated at four (4) different types of location were selected in this study. The
first type is industrial represent by Pasir Gudang, Perai, and Nilai, second type is urban
represent by Kuala Terengganu, Kota Bharu, and Alor Setar. The third is suburban
located in Banting, Kangar, and Tanjung Malim, also the only background station at
Jerantut. The hourly record data from 2010 to 2017 were used to assess the
characteristics and behaviour of O3 concentration. Meanwhile, the monthly record data
of O3, particulate matter (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2),
carbon monoxide (CO), temperature (T), wind speed (WS), and relative humidity (RH)
were used to examine the best MTS models. Three methods of MTS namely vector
autoregressive (VAR), vector moving average (VMA), and vector autoregressive
moving average (VARMA), has been applied in this study. Based on the performance
error, the most appropriate MTS model located in Pasir Gudang, Kota Bharu and
Kangar is VAR(1), Kuala Terengganu and Alor Setar for VAR(2), Perai and Nilai for
VAR(3), Tanjung Malim for VAR(4) and Banting for VAR(5). Only Jerantut obtained
the VMA(2) as the best model. The lowest root mean square error (RMSE) and
normalized absolute error is 0.0053 and <0.0001 which is for MTS model in Perai and
Kuala Terengganu, respectively. Meanwhile, for mean absolute error (MAE), the
lowest is in Banting and Jerantut at 0.0013. The online air quality forecasting system
for O3 was successfully developed based on the best MTS models to represent each
monitoring station
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