Oil palm shell (OPS) is an agricultural solid waste from the extraction process of palm oil. All these wastes from industry pose serious disposal issues for the environment. This research aims to promote the replacement of conventional coarse aggregates with eco-friendly OPS aggregate which offers several advantages, such as being lightweight, renewable, and domestically available. This paper evaluates the mechanical and thermal performances of renewable OPS lightweight concrete (LWC) reinforced with various type of synthetic polypropylene (SPP) fibers. Monofilament polypropylene (MPS) and barchip polypropylene straight (BPS) were added to concrete at different volume fractions (singly and hybrid) of 0%, 0.1%, 0.3% and 0.4%. All specimens were mixed by using a new mixing method with a time saving of up to 14.3% compared to conventional mixing methods. The effects of SPP fibers on the mechanical properties were investigated by compressive strength, splitting tensile strength and residual strength. The strength of the oil palm shell lightweight concrete hybrid 0.4% (OPSLWC–HYB–0.4%) mixture achieved the highest compressive strength of 29 MPa at 28 days. The inclusion of 0.3% of BPS showed a positive outcome with the lowest thermal conductivity value at 0.55 W/m °C. Therefore, the results revealed that incorporation of BPS fiber enhanced the performance of thermal conductivity tests as compared to inclusion of MPS fiber. Hence, renewable OPS LWC was proven to be a highly recommended environmentally friendly aggregate as an alternative solution to replace natural aggregates used in the concrete industry.

Conventional compressive strength test of concrete involves the destruction of concrete samples or existing structures. Thus, the focus of this research is to ascertain a more effective method to assess the compressive strength of concrete, especially during the hardening process. One of the prevalent non-destructive test (NDT) methods that involves the employment of elastic wave has been proposed to forecast the compressive strength development of air-entrained rubberized concrete. The change of the properties, such as wave amplitude, velocity and dominant frequency of the wave that propagates within the concrete is investigated. These wave parameters are then correlated with the compressive strength data, obtained using the conventional compressive strength test. It has been certified that both correlation between wave amplitude and concrete compressive strength, as well as the correlation between velocity and concrete compressive strength, have high regression degrees, which are 0.9404 and 0.8788, respectively. On the contrary, dominant wave frequency has been proved imprecise to be used to correlate with the concrete compressive strength development, as a low correlation coefficient of 0.2677 is reported. In a nutshell, the correlation data of wave amplitude and velocity could be used to forecast the compressive strength development of an air-entrained rubberized concrete in the future.

Concrete is the most used material in the construction industry in modern times, the concrete technology has also been well developed over the years. Carbon emissions from the built environment and its construction has been reported amounting to 38% of the total global emissions. Furthermore, waste management is a struggling issue. This study utilises the recycled expanded polystyrene and kenaf fiber, an agricultural by-product, incorporated into the concrete matrix. Compressive, split tensile and flexural strength has been done on the composite concrete and has shown good results by surpassing the target of 5.2 MN/m2, set according to the Malaysian Standard, MS 76:1972. The interlocking block is a possible application in the construction industry for cost effectiveness.

Wall slip can be defined as a phenomenon where the particles in a suspension moving away from the wall boundary, leaving a thin liquid-rich layer adjacent to the boundary. Such a phenomenon may induce a significant impact on rheological measurements, particularly to viscosity, shear stress, and shear rate. Suspension has wide applications such as food processing, personal care products, pharmaceuticals, paints, medicine, and agrochemical. The traditional technique for the actual shear rate prediction is challenging yet difficult and non-favourable from the perspective of time and cost-effectiveness. Therefore, development of a mathematical computational model which has the ability to perform the prediction task with an acceptable level of accuracy is highly in need. Since radial basis function network (RBFN) has the ability to perform input-output mapping in a highly accurate manner, both of the approaches are employed to generate the actual shear rate prediction model. Through the model evaluation using a series of statistical analyses, it was found that RBFN model V is the best model as it shows the highest coefficient of determination (0.9998), lowest mean squared error (0.001058) and root mean squared error (0.001447), most negative Akaike information criterion (-18426.5) and Bayesian information criterion (-18415.5), and the smallest percentage error.

The dynamics involved in sediment scour are complicated to create a general empirical optimization algorithm to offer reliable sediment load estimation. The existing study was conducted to analyse the architectures of assorted artificial intelligence (AI) based model to forecast suspended sediment load in fluvial system. An in-depth study on traditional approach including Artificial Neural Network (ANN), Adaptive NeuroFuzzy Inference System (ANFIS), and Genetic Programming (GP) was carried out. The goal of this study is to evaluate the performance of AI-based models from various research using SWOT and statistical analyses. Three statistical measures of model prediction accuracy including coefficient of correlation (R), root mean square error (RMSE), and mean absolute error (MAE) were used. The results revealed that the SVM and ANFIS models outperformed the other soft computing and conventional models. It is concluded that the SVM and ANFIS models are preferred and may be successfully used to estimate the suspended sediment concentration for the research area.

Elastic Surface Rayleigh Wave (R-wave) based non-destructive test (NDT) techniques have been studied broadly for concrete surface crack assessment in the past few decades. However, the effect of environment factors on the accuracy of R-wave NDT results are often being neglected. Sand and dust are ones of them that easily accumulate within the surface cracks of concrete structures, which potentially affect the characteristics of R-waves propagation when it travels through the concrete medium that containing cracks. This study is aimed to investigate the effect of concrete surface crack filling conditions to the dominant frequency of R-wave. Numerical simulations were conducted to study the changes of R-wave waveform when it propagated through a cracked concrete model with two various filling conditions, namely fine sand and charcoal powder. Fast Fourier Transform (FFT) for post-signal-processing (PSP) of raw R-wave signals to determine its dominant frequency. The results of numerical simulations were then justified by experimental measurements. It was found that the dominant frequency of R-wave is independent to the volume and the type of filling materials, but sensitive to the surface discontinuity of the concrete.