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.

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Abstract:It is noticeable that the increase of road traffic during the last two decades in addition to the insufficient degree of maintenance caused an accelerated deterioration of road structure. These roads show early signs of distress such as rutting, cracking, low temperature cracking, ageing and stripping. Heavier loads and higher traffic volume demand higher performance of pavement. Excellent performance of pavement requires bitumen that is less susceptible to high temperature, rutting or low temperature cracking. Several additives are used to increase the performance of bitumen and the quality of the produced mixtures. Polymers are considered the most widely used additives in asphalt modification that give better performance. The performance of the Polymer-modified asphalt depends on the type and the level of modification the used polymer. The choice of modification level and t modification type depends on the physical properties of the polymer, and its compatibility with bitumen. The polymer can be loosely classified into two categories, Plastomers and Elastomers. The results indicated that, the addition of polypropylene generally improved the mechanical properties of the mixture regardless of the percentage of polymers that added and (PP) content of 5%. it can be noticed that the performance of PP-modified asphalt mixtures is better compared with unmodified asphalt concrete mixtures modifier because it has the highest Marshall Stiffness, indirect tensile strength and unconfined compressive strength .

Urban land cover classification using Very High Resolution (VHR) satellite images is a very important source of information for map updating. Egyptian environment has more challenges in feature extraction. The main problem lies in the spectral similarity between different land cover classes. Also, great diversity in sizes, shapes, and materials of each class. The main aim of this work is to represent a new automatic indices-based classification method for map updating using VHR satellite images. The method uses a set of spectral indices with their thresholds in consecutive order, chosen based on WorldView-2 (WV-2) bands, to classify land cover in the Egyptian environment. For this study, WV-2 satellite images with eight spectral bands were used. The proposed method is compared with five traditional classification methods; Minimum distance, Spectral angle mapper, Mahalanobis distance, Spectral correlation mapper, and Maximum likelihood method, which included in ERDAS 2015 software, for validation purpose and checking its stability. The results show that the extracted features with the proposed method can contribute significantly to update Egyptian medium scale maps. The average overall accuracy achieved with the proposed approach (75.31%) is higher than those obtained using Minimum distance (54.0%), Spectral angle mapper (69.50%), and Mahalanobis distance (73.63%). Also, it is near to those obtained by the Spectral correlation mapper (76.50%), and Maximum likelihood method (78.25%).

New developed technique is enhanced to recover 3D BIM (Three Dimensions Building Information Models) objects from 3D range surveying data by IR (Infra Red) camera or Laser Scanner for object defects observation. Defects detection and its characteristics have long played major role for condition and risk assessment of buildings. Defects photography by 3D range sensors from an external exposure station, if done mathematically, is useful for interpreting the photos taken by the sensors mounted at anywhere and pointed towards any parts of the building. In this paper an approach was developed to transform and 3D modeling of the buildings defects using multiple types of data and sensors. The advantage of this technique is the flexibility it provides to handle complex defects where a single technique is insufficient. The proposed approach is validated through Jacques Cartier Bridge case study were implemented on the design and inspection data and modeling the building defects by simplification the method of information visualization. Standing on the method, the Defect Information Model (DIM) is in its real shape, which decreases the difficulty and saves the time of modeling the irregular shapes of the defects.

Finite Element Modelling (FEM) has become an increasingly popular method to help researchers find solutions to complex problems of structural mechanics in engineering. Pavement is a complex structure which consists of multiple layers of different materials that influence its behaviour under stress. Rutting behaviour can be predicted by 3D model analysis using the ABAQUS program. The modelling process assumes that the performance of all materials is one of linear elastic behaviour. The main inputs in the modelling process are the material elastic modulus, Poisson's ratio and layer thickness. Models consist of surface, base, subbase and subgrade layers. Subgrade layers are assumed to have infinite depth in all pavement models. This study employed a simulation process of rigid, semi-rigid and flexible pavements using a standard axle load of 80 kN, which represents a single two-wheeled axle. FEM analysis showed that instantaneous vertical displacement along the Z-axis reached 0.105 mm, 0.32 mm and 0.66 mm for rigid, semi-rigid and flexible pavements respectively. Increasing the subgrade elastic modulus from 10 MPa to 200 MPa decreased the vertical displacement by seven, six and a half, and three and a half times for rigid, semirigid and flexible pavement respectively. KENLAYER results refer to the maximum vertical displacements as being 0.1, 0.28 and 0.60 mm for rigid, semi-rigid and flexible pavement respectively. The subgrade elastic modulus is key to improving the resistance to failure of all pavement types. Incremental increase to the subgrade elastic modulus is a potential engineering solution to reducing vertical displacement.

High strength concrete (HSC) is the most widely used material and is presented in many different constructions such as rigid pavement. Concrete has a low tensile strength, limited fatigue life, and is characterized by brittle failure resulting in almost complete loss of loading capacity. HSC reinforced with fibers has displayed great performance in both fresh and hardened states. Recently, the use of single fiber has increased in the rigid pavement and the study of its effect on the properties of concrete. It was found that there is a need to study and compare the effect of adding hybrid fiber to concrete mixture to improve the behavior and properties of concrete. This study investigates the optimization of HSC reinforced with steel fiber by different percentages (0% to 1%), polypropylene fiber (0.0% to 0.26%) and the hybridization of steel fiber and polypropylene fiber as 1% volumetric fractions (0.8% + 0.2%), (0.7% + 0.3%) and (0.6% + 0.4%), respectively. The slump value, compressive strength, tensile strength, and flexural strength are determined. Abrasion resistance and water absorption are also measured. The fiber percentage is adjusted to alter the brittle failure. The results showed that adding fibers to the concrete mixture decrease both slump and abrasion, while increasing permeability, tensile and compressive strength.

Classification is one of the most significant phases for remote sensing image interpretation. All the supervised classifiers need sufficient and efficient training samples, which are usually selected manually and labeled by visual inspection or field survey. Selecting training samples manually requires more time and human effort. A new method is proposed for automatic selection of training samples from a Very High Resolution (VHR) satellite image. The proposed method is tested for selecting training samples automatically for standard supervised pixel-based classification methods instead of manual samples selection. The proposed method uses a set of indices with specific thresholds to identify the training areas for each class. A certain part of each index histogram can be chosen for each class and consider as training samples. Automatic training samples are compared with manual samples for three standard classification methods. The average accuracy achieved by the proposed automatic sample selection is promising; 76.56% for maximum likelihood classifier, 74.06% for spectral correlation mapper classifier, and 70.00% for spectral angle mapper classifier. Although their accuracy scores are slightly nearby classification with manually selected samples by an average of 1.74% for maximum likelihood, 2.44% for spectral correlation mapper, and 3.75% for spectral angle mapper classifier.