The suitability of coconut shell concrete as a replacements in term of mechanical and thermal properties – a review

The most critical issue in environment protection and natural resource conservation is waste management [1]. Changes in environment and an increase in population are the main causes of the many processes of deterioration which have altered the ecosystem of our planet, including the generation of municipal solid waste (MFS) [2]. Therefore, there is a need to reuse waste to create a greener and healthier place on earth. The usage of agricultural waste will be emphasized in this research. Being renewable, low-cost, lightweight, having high specific strength and stiffness have made agricultural waste ideal for use as construction materials [3]. Coconut shell, oil palm shell, oil palm clinker, corncob ash, and rice husk ash are all agricultural by-products. Although some of these materials can be used as animal feed or fuel in biomass power plants or boilers of various industrial sectors to produce steam, a lot of these materials are still disposed off into landfills or burnt. This leads to serious environmental problems..

Potential of utilizing asphalt dust waste as filler material in the production of sustainable Self Compacting Concrete (SCC)

Waste materials from many industries are widely used in the production of sustainable green concrete. Utilizing asphalt dust waste (ADW) as a filler material in the development of self-compacting concrete (SCC) is one of the alternative solutions for reducing environmental waste. SCC is an innovative concrete that does not require vibration for placing and compaction. However, there is limited information on the effects of utilizing ADW in the development of SCC. Therefore, this research study examines the effects of various w/b ratios (0.2, 0.3 and 0.4) and differing amounts of ADW (0% to 50%) on the rheological properties of fresh state concrete. The compressive strength of the SCC was tested only for 7 and 28 days as preliminary studies. The results revealed that mixtures MD730, MD740 and MD750 showed satisfactory results for the slump flow, J-Ring, L-Box and V-Funnel test during the fresh state. The compressive strength values obtained after 28 days for MD730, MD740 and MD750 were 35.1 MPa, 36.8 MPa and 29.4 MPa respectively. In conclusion, the distribution of materials in mixtures has significant effect in achieving rheological properties and compressive strength of SCC

The Behavior of Non-Destructive Test for Different Grade of Concrete

Rebound hammer are prefer as non-destructive testing methods; where compression test as destructive test. A general series of tests for rebound hammer and destructive test was carried out at heavy concrete laboratory to obtain the desire result. A set of concrete cubes of sizes 100 x 100 x 100 mm  had been casted and subjected to water curing which was held for 7, 14, 21 and 28 days  to get the exact result of cube strength and rebound number. Rebound hammer testing were initially done before the compression test. The data obtained from each test has been evaluated and tabulated in  this report. From this research, the variation between predicted strength and experimental strength for rebound hammer testing was 1.6%. This indicated rebound hammer testing managed to predict  the strength more accurately. However, non-destructive test shown a margin of less than 10% error compare to destructive test

Analysing method for acoustic emission clustering system on reinforced concrete beam

Acoustic Emission (AE) is a non-destructive testing (NDT) method used for damage detection in structural engineering. Nowadays, NDT is widely used especially on continuous real-time monitoring systems with minimum labour involvement. It could also be used to discriminate the different types of damage occurring in reinforced concrete (RC) beam. In spite of these advantages, difficulties still exist in using the AE technique for monitoring applications particularly in analysing recorded AE data due to the large quantity of data involved. Other than that, the main problem associated with data analysis is the discrimination between different AE sources and the analysis of AE signals in order to identify the most critical damage mechanism. Clustering analysis is a technique in which a set of objects are assigned to a group called cluster. The need for effective data analysis in the clustering system can be linked to three main objectives in this research; (1) to determine the type of failure on reinforced concrete beams through the AE system; (2) to identify and discriminate the AE data parameters via crack classification (tensile and shear movement); (3) to verify the crack classification of Rise Amplitude (RA) clustering by using the NI LabVIEW clustering algorithm. Hence, the purpose of this research is to obtain the crack classification by using the RA clustering analysing (RAC) method. It was found that, the result by using RAC analysing method was reliable system to cluster the cracking on RC beam. In addition, these analysing system could use in any different sizing of beam

A Preliminary Study Application Clustering System in Acoustic Emission Monitoring

Acoustic Emission (AE) is a non-destructive testing known as assessment on damage detection in structural engineering. It also can be used to discriminate the different types of damage occurring in a composite materials. The main problem associated with the data analysis is the discrimination between the different AE sources and analysis of the AE signal in order to identify the most critical damage mechanism. Clustering analysis is a technique in which the set of object are assigned to a group called cluster. The objective of the cluster analysis is to separate a set of data into several classes that reflect the internal structure of data. In this paper was used k-means algorithm for partitioned clustering method, numerous effort have been made to improve the performance of application k-means clustering algorithm. This paper presents a current review on application clustering system in Acoustic Emission

A Preliminary Study Application Clustering System in Acoustic Emission Monitoring

Acoustic Emission (AE) is a non-destructive testing known as assessment on damage detection in structural engineering. It also can be used to discriminate the different types of damage occurring in a composite materials. The main problem associated with the data analysis is the discrimination between the different AE sources and analysis of the AE signal in order to identify the most critical damage mechanism. Clustering analysis is a technique in which the set of object are assigned to a group called cluster. The objective of the cluster analysis is to separate a set of data into several classes that reflect the internal structure of data. In this paper was used k-means algorithm for partitioned clustering method, numerous effort have been made to improve the performance of application k-means clustering algorithm. This paper presents a current review on application clustering system in Acoustic Emission