The usage of recycled materials in concrete has become popular recently. This paper focuses on a study related to the bonding between steel and concrete containing expanded polystyrene beads (EPS) and palm oil fuel ash (POFA) as replacement material. The EPS were used as fine aggregate replacement, and POFA was used as cement replacement. The replacement percentages for EPS and POFA in the concrete were limited to a range of 0-30% and 0-10%, respectively. Previous studies have identified the potential of POFA and EPS as concrete substances. The typical issue with EPS-containing concrete is its characteristic weakness, which leads to a compromised bond with steel. This occurs because EPS fails to effectively interact with cement, resulting in a weak bond and low compressive strength. Consequently, in this study, POFA is introduced as an addition to enhance the bond strength between EPS-containing concrete and steel. Pull-out tests in this study seem to represent the bonding performance between concrete and steel. The 10% of POFA in concrete seems might improve its performance in terms of compression strength, and bonding between concrete and steel.

Due to the country's expanding population and the resulting growth of the building industry, brick is one of the most crucial materials used in construction projects in Malaysia. Producing large amounts of bricks requires a lot of cement yet this method has environmental consequences due to increasing carbon emissions into the atmosphere. Therefore, this study aims to use partial Sago Fine Waste (SFW) to replace the cement. About 60 tonnes of sago trash are dumped into the closest river every day during the production of sago starch. To protect the environment and contributing to sustainable development, this study has been conducted on the production of bricks from waste materials. For this study, the brick specimens were prepared using 0%, 1%, 3%, 5%, 7% and 9% of SFW with a water-cement ratio of 0.6 fixed at 1:3 sand cement ratio. The total specimens that were produced for testing are 72 bricks. The water curing for concrete has been conducted at 7 and 28 days. The overall results revealed that both density and compressive strength are decreases as the percentages of SFW increases. The initial rate of absorption increases due to the increasing percentage of SFW. However, all the results obtained are still met the requirements. Based on the findings, the optimum percentage SFW are SFW1W0.6 with strength 5.18 MPa. The optimum brick properties of SFW1W0.6 is normal weight with density 2092.86 kg/m3 and lowest initial rate absorption with 0.91 kg/m3.min.

Drying shrinkage, the reduction in volume as a material like cement mortar dries, can result in cracks and decreased durability. Bamboo biochar powder (BCP) serves as a substitute for cement in mortar, affecting its drying shrinkage characteristics. Research has shown that BCP in cement mortar can alleviate drying shrinkage by absorbing and retaining moisture. This study aims to assess the chemical and physical properties of BCP, determine the mechanical attributes of bamboo biochar mortar with varying percentages of cement replacement, and investigate the impact of BCP on mortar shrinkage in indoor and outdoor tropical conditions. BCP, derived from Gigantocholoa Abociliata species and sized at 75 mu m, was used to replace cement rates of 0%, 5%, 10%, 15%, and 20%. Sixty cube samples (50x50x50mm) were employed for density, ultrasonic pulse velocity (upv), compressive strength, and water absorption tests. Additionally, thirty prism samples (100x100x400mm) were employed to assess drying shrinkage during outdoor and indoor exposure, spanning up to 150 days. The experimental data indicates a consistent trend as the percentage of cement replacement increases in the mortar mix, density, and compressive strength decrease, while UPV and water absorption increase. The lowest shrinkage strain was observed during indoor exposure with 5% cement replacement, attributed to BCP acting as a filler, creating strong bonding properties, and reducing shrinkage. Conversely, the highest strain was noted during outdoor exposure with 20% cement replacement, resulting from higher moisture loss. In summary, a 5% replacement of cement with BCP in mortar offers the most effective reduction in shrinkage strain.

The root cause of the majority of structural failure is attributed to cracking, there is a compelling economic incentive to develop concrete that can treat and repair the damage by itself. Even though some research has been carried out in this area a breakthrough in method to supply nutrient for effective healing are yet to materialise. For the present study, Bacillus sphaericus with diatomaceous earth as a protective vehicle was selected to determine the best performance with different curing media as an alternative method to supply a nutrient. For the growth of the bacterial, the nutrients are supplied using three different curing media which is deposition medium (controlled nutrients), runoff water (uncontrolled nutrients) and also distilled water. The performance of Bio-based cement mortar (BBCM) was evaluated by comparing the influence on compressive strength and water absorption subjected to different curing media. BBCM cured in run-off water had the best compressive strength with increment of 39.04% compared to others curing media. Also, with its great dispersal characteristics, denser BBCM with a reduction of water absorption. BBCM cured in run-off water had a 40% improvement in strength compared to normal curing. As a conclusion, run-off water is highly promising in supplying sufficient nutrients to bacteria for the biomineralization process to produce CaCO3. This work also aims to apply this approach in the field especially in sewerage and drainage system.

The implementation of sustainable construction and green building becomes the main attention of construction industries in Malaysia as it has been introduced by the government in the Construction Industry Transformation Programme (2016-2020). Therefore, this study focuses on the development of sustainable concrete bricks containing Expanded Polystyrene (EPS) and Palm Oil Fuel Ash (POFA) as sand and cement substitute materials. The percentage of replacement is 20%, 30%, 40% and 50% for EPS and 5%, 10%, 15%, 20% and 25% for POFA. There are 30 different mix designs of brick have been produced and their properties have been identified. Hardened brick density, compressive strength, water absorption and initial rate of absorption are the brick properties identified in this study. Based on the experimental results, it was found that the hardened brick density and compressive strength of the brick decreased as the replacement percentage increased. On the other hand, the water absorption and initial rate of absorption of the brick decreased as the percentage of EPS increased and increased as the percentage of POFA increased. Based on the findings, it shows that EPS and POFA has significantly contributes to the reduction of brick density. Next, for the compressive strength all the bricks have satisfied the minimum strength requirement of non-load bearing brick. Finally, for water absorption and initial rate of absorption, it has been found that majority of the bricks have an acceptable value based on standard requirements for brick. This can be concluded that EPS and POFA could be potential substitute materials for the manufacture of sustainable bricks.

Major noise and vibration during train operation can cause disturbance to the surrounding. One of the methods to reduce this disturbance are by installing concrete sleepers. The use of railway concrete sleepers may be a high potential to reduce the noise and vibration. To produce concrete sleepers cement usage will be used with greater volume. Approximately 100 million tons of Palm Oil Fuel Ash (POFA) was disposed to the landfill currently. POFA contains high silica content and porous particles which indicated its pozzolanic properties and sound absorption characteristics. Therefore, this study was to determine the sound absorption coefficient of railway concrete sleepers containing POFA as a cement replacement material. Concrete sleepers with a strength grade of 55 and a w/c ratio of 0.35 were prepared in this study. Three design mixes with 0% (control), 20%, and 40% of POFA tested by using an impedance tube test at 28 days of curing age. The results show, the sound absorption coefficient and noise reduction coefficient increases as the percentage of POFA increases. The best performance was obtained by concrete sleepers containing 40% of POFA, with a recorded sound absorption coefficient of 0.10 for low frequency and 0.44 for high frequency. Meanwhile, the noise reduction coefficient recorded was 0.33, which reduce 32% of noise compared to OPC.

The present study aimed to optimize the artificial neural network (ANN) with one of the well-established optimization algorithms called particle swarm optimization (PSO) for the problem of ground response approximation in short structures. Various studies showed that ANN-based solutions are a reliable method for complex engineering problems. Predicting the ground surface respond to seismic loading is one of the engineering problems that still has not received any ANN solution. Therefore, this paper aimed to assess the application of hybrid PSO-based ANN models to the calculation of horizontal deflection of columns in short building after being subjected to a significant seismic loading (e.g., The Chi-Chi earthquake used as one of the input databases). To prepare both of the training and testing datasets, for the ANN and PSO-ANN network models, a series of finite element (FE) modeling were performed. The used FEM simulation database consists of 8324 training datasets and 2081 testing datasets that is equal to 80% and 20% of the whole database, respectively. The input includes Chi-Chi earthquake dynamic time (s), friction angle ( φ ), dilation angle ( ψ ), unit weight ( γ ), soil elastic modulus ( E ), Poisson’s ratio ( v ), structure axial stiffness (EA), and bending stiffness (EI) where the output was taken horizontal deflection of the columns at their highest level ( U x ). The result indicates higher reliability of the PSO-ANN model in estimating the ground response and horizontal deflection of structural columns in short structures after being subjected to earthquake loading.

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (Id), embedment ratio (L/D), both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts.

This paper investigates the stress strain behaviour of concrete containing Palm Oil Fuel Ash and Expanded Polystyrene, axial compressive strength. tensile strength and modulus of elasticity. EPS-POFA concrete was prepared by substituting fine aggregates with EPS beads and cement replaced with POFA by 10%, 20% and 30%. Results of this study showed that EPS-POFA concrete exhibited low axial compressive strength, peak strain, tensile strength and elastic modulus when the EPS and POFA contents in concrete increased. However, the decrease in axial compressive strength of concretes with containing 10 to 20% EPS and POFA are suitable amount and acceptable to be applied on building structure as per stated in America Concrete Institute 318 with minimum specified compressive strength for structural concrete is 2500 psi (17 MPa). While, the failure of EPS-POFA concrete under axial compression gradually occurred and the concretes were able to retain the load after failure without full collapse. The slope of stress-strain curve of concretes with containing EPS and POFA was lower than that of normal concrete, demonstrating that the normal concrete more brittleness that EPS-POFA concretes.

In this study, we employed the most suitable artificial intelligence systems and optimized it with a novel evolutionary algorithm called particle swarm optimization (PSO) for the problem of cooling load ( C L ) in energy-efficient building (EEB) system. Then, the mentioned methods are utilised to identify a relationship between the input and output parameters of the EEB system. The amount of C L was taken as the essential output of the EEB system, while the input parameters were channel length, channel depth, channel width, and air mass flow rate. The predicted results for data sets from each of the abovementioned models were evaluated according to several known statistical indices such as correlation coefficient ( R 2 ), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of colour intensity rating and total ranking method. The M5Rules has been proposed as the best predictive network in this study. The results of the M5Rules network indicated the R 2 , MAE, RMSE, RAE, and RRSE for the training and testing data sets were 0.9982, 0.0426, 0.0653, 6.2344, and 6.0298 and 0.7626, 0.9903, 0.9593, and 0.9981, respectively. According to R 2 , RMSE, and VAF, values of 0.99983, 0.0066, and 99.98 and 0.9982, 0.065, and 84.5 were obtained for testing data set and values of proposed PSO-M5Rules prediction network models, respectively. This indicates higher reliability of the introduced PSO-M5Rules model in approximating C L of an EEB system.

Blasting is the predominant rock fragmentation technique in civil constructions, underground and surface mines. Flyrock is the unwanted throw of rock fragments during blasting and is the major cause of considerable damage in and around the mines. The present research aimed to propose a new intelligence-based method to predict flyrock. In this regard, the recurrent fuzzy neural network (RFNN) combined with the genetic algorithm (GA) is proposed. For checking the suitability of the RFNN-GA model, artificial neural network (ANN), hybrid ANN and GA and a nonlinear regression model were also employed. To achieve the aims of the research, data for 70 blasting sites including four input parameters (spacing, burden, stemming and maximum charge per delay) and one output parameter (flyrock) were gathered from two quarry mines at the Shur River dam, Iran. The performance of the proposed prediction methods was then assessed with statistical evaluation criteria, i.e., R-square and root mean square error. The results indicate the proposed RFNN-GA model was more superior for prediction of flyrock than the GA-ANN, ANN and nonlinear regression models. According to a sensitivity analysis, the maximum charge per delay was the most influential parameter in flyrock prediction in this case.

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

In this study, we optimized artificial neural network (ANN) with imperialist competition algorithm (ICA) for the problem of slope stability design charts. To prepare training and testing datasets for the ANN and ICA–ANN predictive models, an extensive number of limit equilibrium analysis modelings (e.g., for the lower bound, LB, limit analysis and upper bound, UB, limit analysis) was conducted. The analyses were conducted using OptumG2 computer software and implemented on two-layered cohesive soil layer sets. For each of the LB and UB limit analysis, the database consisted of 320 training datasets and 80 testing datasets. Variables of the ICA algorithm such as the number of countries, the number of initial imperialists and the number of decades were optimized using a series of trial-and-error process. The input parameters that used thorough the OptumG2 finite element modeling (FEM) analysis include depth factor (i.e., the ratio of first soil layer thickness to the slope height), slope angle, undrained shear strength ratio where the output was taken dimensionless stability number. The estimated results for both of datasets (e.g., training and testing) from ANN and ICA–ANN models were assessed based on three known statistical indices namely value account for (VAF), root means squared error (RMSE), and coefficient of determination (R2). To evaluate the performance of proposed models, color intensity rating (CER) and total ranking method (TRM), i.e., based on the result of statistical indices, was used. After 72 trial-and-error processes (e.g., sensitivity analysis on some neurons) the optimal architecture of 3 × 6 × 1 were found for both of the ANN–UB and ANN–LB models. As a result, both models presented excellent performance, however according to the introduced ranking system the ICA–ANN model could slightly perform a better performance compared to ANN. Based on R2, RMSE and VAF values of (0.9999, 0.0107 and 99.9924) and (0.9991, 0.0102 and 99.9913), respectively, were found for training and testing of the optimized ICA–ANN–LB predictive model. Similarly, for the ICA–ANN–UB predictive model, values of (0.9984, 0.0129 and 99.9659) and (0.9984, 0.01047 and 99.9915) were obtained for the R2, RMSE and VAF of training and testing datasets, respectively. However, in the ANN model, the R2 and RMSE for both of the training and testing datasets were (0.9982 and 0.01815) and (0.9972 and 0.01748), respectively. This proves a better performance of the ICA–ANN model in predicting the behaviors of slope stability of cohesive soils and consequently more reliable design solution charts provided herein.

Introducing fibres into concrete not only enhances the requisite properties of reinforced concrete but also changes the material characteristic from brittle to ductile failure. Steel fibre (SF) is found to enhance the flexural and tensile strengths, and at the same time is able to resist the formation of macro cracking and concrete spalling. Meanwhile, polypropylene fibre (PPF) will contribute to the tensile strain capacity and compressive strength, and also delaying the formation of micro cracks. Therefore, the mechanical properties of concrete are discussed in this paper of which SF and PPF are combined and mixed together in concrete. For this purpose, hooked-end type deformed steel fibre and fibrillated type virgin polypropylene fibre are used in the experimental work where the concrete strength is maintained for grade C30. The specimens incorporated with SF and PPF are different in terms of its percentage proportions, however, the total volume fraction is fixed at 1.5%. The experimental result shows that the percentage proportion containing 75% SF and 25% PPF produced the highest flexural and tensile strengths with an increase of 45.89% and 25.93%, respectively than that of the plain concrete. For flexural toughness, specimen with 100% SF and 0% PPF produced the highest value of 66.6 Joule. Meanwhile, for compressive strength, there is not much contribution for both SF and PPF to the mechanical properties of the concrete.

This paper discusses the experimental results on the flexural test of concrete containing different proportions of steel fibre (SF) and polypropylene fibre (PPF). The flexural test was carried out under 4-point bending load and followed the relevant standards to FRC. Hooked-end deformed SF fibre with 60 mm length and fibrillated virgin PPF fibre with 19 mm length were used in this study. Meanwhile, the concrete was designed for high strength concrete of C60. The mixture included both single SF and PPF, and also the combination of both fibres; Control beam (PC), beam with 75%SF, beam with 75%SF + 25%PPF and beam with 25%PPF. The total fibre volume fraction (Vf) was fixed at 1.5%. The experimental results show that the percentage proportion of combined SF-PPF at 75-25% had the best performance for its flexural capacity. Mixture with single PPF was also found not effective in delaying the onset of tension cracks and to increase the tensile strength of the concrete. Experimental result also shows beam with 75%SF +25%PPF had their structural stiffness improved the most as compared with the others. For the compressive strength, beam with 75%SF + 25%PPF also revealed comparable performance with the control for high strength composite concrete.

An experimental study had been carried out to investigate the mechanical properties, expansion and shrinkage of fibre reinforced concrete composite (FRC). However, instead of using single type fibre of either steel (SF) or polypropylene (PPF), this study also combined the two types in one mix.The mechanical properties investigated in this study include compressive strength, splitting tensile strength and flexural strength. Three different FRC mix proportions and one normal concrete (control) were casted which includes (a) 75% SF, (b) 75% SF + 25% PPF, (c) 25% PPF, and (d) 0% fibre for control (PC). Meanwhile, the volume fraction, Vf for the FRC was fixed at 1.5% and the concrete strength was designed to achieve grade C60 at 28 days. The results show that the use of fibres in concrete decreased the workability of concrete. In addition, concrete mix with both SF and PPF produced the highest splitting tensile and flexural strengths by an increase of 75.9% and 86.5%, respectively as compared with the control. Furthermore, expansion and shrinkage of FRC was found to be less than the control. It can be concluded that the combined SF and PPF in concrete gives the most appropriate combination as regards to the highest flexural and splitting tensile strengths, and also reduced the shrinkage strain.

Introducing fibres into concrete not only enhances the requisite properties of reinforced concrete but also changes the material characteristic from brittle to ductile failure. Steel fibre (SF) is found to enhance the flexural and tensile strengths, and at the same time is able to resist the formation of macro cracking and concrete spalling. Meanwhile, polypropylene fibre (PPF) will contribute to the tensile strain capacity and compressive strength, and also delaying the formation of micro cracks. Therefore, the mechanical properties of concrete are discussed in this paper of which SF and PPF are combined and mixed together in concrete. For this purpose, hooked-end type deformed steel fibre and fibrillated type virgin polypropylene fibre are used in the experimental work where the concrete strength is maintained for grade C30. The specimens incorporated with SF and PPF are different in terms of its percentage proportions, however, the total volume fraction is fixed at 1.5%. The experimental result shows that the percentage proportion containing 75% SF and 25% PPF produced the highest flexural and tensile strengths with an increase of 45.89% and 25.93%, respectively than that of the plain concrete. For flexural toughness, specimen with 100% SF and 0% PPF produced the highest value of 66.6 Joule. Meanwhile, for compressive strength, there is not much contribution for both SF and PPF to the mechanical properties of the concrete.

This paper discusses the experimental results on the mechanical properties of hybrid fibre reinforced composite concrete (HyFRCC) containing different proportions of steel fibre (SF) and polypropylene fibre (PPF). The mechanical properties include compressive strength, tensile strength, and flexural strength. SF is known to enhance the flexural and tensile strengths, and at the same time is able to resist the formation of macro cracking. Meanwhile, PPF contributes to the tensile strain capacity and compressive strength, and also delay the formation of micro cracks. Hooked-end deformed type SF fibre with 60 mm length and fibrillated virgin type PPF fibre with 19 mm length are used in this study. Meanwhile, the concrete strength is maintained for grade C30. The percentage proportion of SF-PPF fibres are varied in the range of 100-0%, 75-25%, 50-50%, 25-75% and 0-100% of which the total fibre volume fraction (Vf) is fixed at 0.5%. The experimental results reveal that the percentage proportion of SF-PPF fibres with 75-25% produced the maximum performance of flexural strength, tensile strength and flexural toughness. Meanwhile, the percentage proportion of SF-PPF fibres with 100-0% contributes to the improvement of the compressive strength compared to that of plain concrete.