Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments

The use of Artificial Intelligence (AI) for the prediction of flexible pavement maintenance that is caused by distressing on the surface layer is crucial in the effort to increase the service life span of pavements as well as reduce government expenses. This study aimed to predict flexible pavement maintenance in tropical regions by using an Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) for predicting models for pavement maintenance in the tropical region. However, to predict the performance of the treatment techniques for flexible pavements, we used critical criteria to choose our date from different sources to represent the situation of the current pavement. The effect of the distress condition on the flexible pavement surface performance was one of the criteria considered in our study. The data were chosen in this study for 288 sets of treatment techniques for flexible pavements. The input parameters used for the prediction were severity, density, road function, and Average Daily Traffic (ADT). The finding of regression models in (R2) values for the ANN prediction model is 0.93, while the (R2) values are (RSM) prediction model dependent on the full quadratic is 0.85. The results of two methods were compared for their predictive capabilities in terms of the coefficient of determination (2), the Mean Squared Error (MSE), and the Root Mean Square Error (RMSE), based on the dataset. The results showed that the prediction made utilizing ANN was very relevant to the goal in contrast to that made using the statistical program RSM based on different types of mathematical methods such as full quadratic, pure quadratic, interactions, and linear regression

A Review of the Utilisation of Recycled Waste Material as an Alternative Modifier in Asphalt Mixtures

The possibility of using waste materials in road construction is of great interest as their utilisation may contribute to reducing the problems of hazard and pollution and conserve natural resources. Thus, there is an urgent need to find a sustainable method for using waste materials as a substitute in the standard asphalt binders. There are several concerns about the physical and chemical properties and mechanical performance of asphalt pavements incorporated with waste material in the effort to reduce permanent deformation of the road surface. This review article presents a brief discussion of the asphalt mixtures modified with waste material, and the recycled materials used as a modifier in the asphalt mixture. The present paper summarises the use of crumb rubber, crushed concrete, steel slag, glass fibre and plastic waste in asphalt mixtures. The use of waste materials as a modifier in asphalt mixture resulted in improved asphalt pavement performance. Results advocate that rubberised asphalt mixture with desired properties can be designed as an additive with a friendly environmental approach in construction materials. The researches that adopted the influence of usage, recycle waste material to improve the performance of the asphalt of the road are still limited compared to other construction fields. Doi: 10.28991/cej-2020-SP(EMCE)-05 Full Text: PD

Predicting the rheological properties of bitumen-filler mastic using machine learning techniques

This study uses the artificial neural network and response surface methodology to develop two models for predicting the rheological properties, complex modulus (G*) and phase angle (δ) of bitumen-filler mastic. It also analyses and evaluates the accuracy of both models by determining the coefficient of determination (R2), mean squared error (MSE), and root mean squared error (RMSE). The prediction models use the G* and δ data from a previous study by researchers at the Nottingham Transportation Engineering Centre to determine three types of bitumen-filler mastic (limestone, cement and grit stone) with varying filler concentrations of 15, 35, 40 and 65%. The analysis shows that both models perform well in predicting the rheological properties of bitumen-filler mastic. A comparison of the two models shows that the artificial neural network (ANN) has higher accuracy than the response surface methodology model, with an R2 value exceeding 0.92. The results of the ANN achieve a higher R2 value and lower MSE and RMSE values. In summary, the performance of the artificial neural network model is better than the response surface methodology model, which uses the full quadratic, pure quadratic, linear and interaction mathematical methods

Stiffening Effect of Fillers Based on Rheology and Micromechanics Models

The aggregate in an asphalt mixture is coated with mastic consisting of bitumen (dilute phase) and filler (particulates phase). The interaction of bitumen and filler and packing of filler plays an important role in the properties of mastics. The micromechanics models from composite rheology can be used to predict the stiffening effect of a suspension. In this research, the stiffening effect of fillers was investigated based on the rheology of mastic. The frequency sweep tests in a dynamic shear rheometer at different temperatures were performed within a linear viscoelastic range to construct the master curves. The volume fractions were expressed as compositional volumes of filler in mastic. The particle shape and surface texture are determined through microscopy. We used six micromechanics-based models to predict the stiffening potential of fillers in mastics. The models include Maron–Pierce, Lewis Nielsen, Mooney, Krieger–Dougherty, Chong, Robinson, and Hashin Models. The results show that the same volume content of filler has a different effective volume. The fillers increase the stiffening effect of the composite, especially at high temperatures. The behaviour of fillers with similar effective volume and packing is identical. The filler type affects the stiffening of mastics. Micromechanics modelling results show that most models show an accurate stiffening effect at lower concentrations with the exception of the Chong Model. The Maron–Pierce Model under-estimates the stiffening potential for granite mastic at higher concentrations beyond the 30% filler content fraction. The value of maximum packing fraction (ϕm) and Einstien coefficient (KE) in the Mooney model are significantly different from other models for limestone and granite, respectively. The line of equality graph shows good agreement of measured and predicted stiffness. It is difficult to precisely model the mastic data with any single model due to the presence of complex stiffening effects beyond volume filling

Hybrid Timber Concrete Composite Slab for Analysis of Lag Screw Embedment Connections

Push-out-shear tests were used in this study to analyze lag screw connections in timber-concrete composite (TCC) slabs based on the embedment depth. The goal of this research is to look into the relationship between shear capacity and embedment depth in TCC, as well as to investigate the embedment strength of the wood. Experiments were carried out at different embedment depths (5.08 cm, 7.0 cm, and 8.9 cm). The prepared samples were examined in order to determine the failure modes and provide an accurate assessment of the influence of embedment depth on TCC slabs. The investigation on the embedment strength of the wood was performed then for the analysis of the crushing of wood fibers, lag screw yielding strength, and maximum load applied at embedment depths of 6.6 cm and 7.0 cm. The results indicate that between 5.08 cm and 7.0 cm, there was an apparent improvement in the relationship between embedment depth (ED) and shear capacity of TCC slabs in terms of the shear strength, while a significant difference was observed between 7.0 cm and 8.9 cm. The study suggests that the ED of the TCC slab should be maintained at around 7.33 times the diameter of the lag screw

Application of paper sludge ash and incinerated sewage ash in emulsified asphalt cold mixtures

Certain disadvantages could have appeared while using hot mix asphalt (HMA), such as the release of unhealthy gases into the environment (environmental issues), difficulty in sustaining the temperature over long distances (logistical issues), and consuming a sufficient amount of energy while preparing and laying down (practical and economic issues). To overcome the aforementioned issues, this study aimed to develop rapid-curing emulsified asphalt cold mixes (EACM) comprising a cementitious filler made from industrial by-product materials. Paper sludge ash (PSA) is used as an active filler for application in the EACM rather than conventional mineral filler. Additionally, to maximize the effect of PSA’s hydraulic activity, incinerated sewage ash (ISA) is utilized as an activator at a concentration of 0%–4% by mass of the aggregates. The results demonstrate that the use of waste PSA significantly improves the indirect tensile stiffness modulus (ITSM) by around 10 times more after 2 days than the traditional emulsified asphalt cold mixes. In addition, the improvement in ITSM was around 30% and 65% for 6%PSA+1%ISA and 6%PSA+4%ISA mixes, respectively. Furthermore, the rutting for the 6%PSA+1%ISA and 6%PSA+4%ISA mixes decreased to around 19% and 11% in comparison to the traditional 131-pen HMA. The formation of hydration products and rapid demulsification of asphalt emulsion, which results in binding within the mixtures, are responsible for the increased ITSM and rutting resistance. As a result, environmental issues are minimized, and energy preservation may be maintained

Investigating behaviour of reinforced concrete with glass fibre

Concrete is the most commonly used building material. Nowadays, the world has seen the construction of engineering applications that has become difficult and complicated. Therefore, it is important to have high strength and adequate workability. Besides that, the glass fibre is highly beneficial as a construction material for reinforced concrete as it can be identified as one of the numerous compelling topics related to its benefits. This study contributes to the specification and classification of glass fibre reinforced concrete (GFRC). However, ordinary concrete has limited ductility, slight resistance to cracking, and insufficient tensile strength. Internal micro-cracks in the concrete are visible, and the proliferation of such micro-cracks caused its weak tensile strength. When a certain percentage of fibre is added to the concrete, it improves the properties of the strain, namely, resistance to cracking, ductility, toughness, and flexure strength. The current paper outlines the experimental study conducted on the usage of glass fibre with structural concrete. The parameters were used in percentages, which varied from 0.5% to 2% by weight of cement in concrete, and the properties of the FRC (fibre reinforced concrete), such as ultrasonic pulse velocity test, flexure strength, and compressive strength were examined. However, it refers to an increase in deformation before failure of the structural concrete, reinforced with a high ratio of GFR. The results show good performance of concretes containing glass fibre and increasing glass fibre content, increasing the compressive strength

Bituminous Pavement Reinforcement with Fiber: A Review

This paper attempts to display, analyze and discuss the literature affiliated to the previous research data on road surfacing in pavement engineering reinforcement. In this paper, a review of the background and present status of road surfacing is also provided for supportive explanation of the significance of fiber-reinforced asphalt pavement HMA and its role in providing effective and durable surfacing for heavy-trafficked roads. The paper attempts to clarify some of the terms and notions related to the discussions to give the readers the needed background, to be able to actively understand the experiments and discussions. Results from many studies confirm that fiber specifically enhances the optimum bitumen content in the design of the mixture and halts the bitumen leakage due to its asphalt absorbing susceptibility. Fiber modifies the visco-elastic response, susceptibility against moisture, increase resistance to rutting, as well as lowers the pavement fatigue cracking

Measuring the characteristics among critical success factors of PPP infrastructure projects

Private-Public Partnership (PPP) is an increasingly popular choice for policymakers in implementing critical public projects through the examination of essential factors of success of PPP and establishing an empirical model of PPP in the construction project in Malaysia. The PPP implementation model was hypothesised to investigate the measurements and dimensions of technology, Organisational, and project characteristics as critical success factors of PPP implementation. A quantitative methodology was employed to validate the measurements, hypothesis testing and validate a structural model of PPP implementation. A total of 238 respondents was involved in the survey of the hypothesised PPP model. SPSS version 22 as well as Analysis of Moment Variance (AMOS) software were employed to analyse the data gathered. Path analysis and mediated regression analysis of the structural equation model succeeded in determining the mediating effect of stakeholder and procurement on the relationship among critical success factors and PPP implementation. The overall results show a significant positive interaction of Organisational, technical, and project characteristics as essential factors of success on stakeholder and procurement as a mediator on the achievement of PPP implementation. This paper highlights not only the vital success factors for PPP but also offers a fundamental contribution model achieved through the empirical model of critical success factors and PPP implementation in the construction project in Malaysia. This study succeeds in establishing and validating a structural model of the PPP implementation model. The model contributes to the body of knowledge of PPP and benefits to practitioners as primary guidance on construction and business developments