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An entire roadway system represents a crucial element in the sustainable urban transportation planning process. Pavement surfaces are at continual risk of accumulating serious deteriorations and defects throughout their service life due to traffic loading and environmental impact. Since roadway networks are growing rapidly, relying on visual pavement inspection is not always feasible. Therefore, this paper proposes an effective assessment method for evaluating flexible pavement surface distresses using a terrestrial laser scanner (TLS) and calculating the pavement condition index (PCI). The proposed terrestrial laser scanner method results in road condition assessments becoming faster, safer, and more systematic. It also aims to determine the geometric characteristics of the investigated roads. A major road in Egypt was selected to test the proposed technique and compare it with the traditional visual inspection method. The evaluation was carried out to assess different types of pavement distress, such as cracking, rutting, potholes, and raveling distresses. Every pavement distress was defined in terms of surface area, the width of the crack, and intensity, and the data from TLS were then processed by MAGNET COLLAGE software. A MATLAB program was developed to match the TLS observational data to plane equations. PAVER software was also used to determine the PCI values for each TLS position. The revealed distresses for the investigated road using TLS observations reveal a significant improvement in determining flexible pavement distresses and geometric characteristics.

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PDF

The article presents a novel computation technique for predicting the settlement of reinforcement concrete (R.C) raft foundations based on total station and precise leveling measurements. Modified inverse distance weight (MIDW) and principal component analysis (PCA) are developed to predict the nonmeasurable settlement of raft foundations and are compared to regression techniques. Wall measurements are used to verify the proposed interpolation methods. Six observation epochs were carried out over 3 years for the R.C raft foundation of a multistory building in Baltim, Egypt, which rests on clay soil, to monitor and predict the foundation settlement. The obtained results show that PCA and MIDW models outperform other models in interpolating nonmeasurable (invisible) points, while the accuracy of MIDW is the best. The developed techniques can be used to interpolate nonmeasurable (invisible) points to understand the actual behavior of foundation settlement. The monitoring building showed that the slope of the foundation in the X direction is 7.9 and 14.5% after 5 and 26 months, respectively.

The main task of this study is to introduce an applied methodology using terrestrial laser scanner for facilitating the process of detecting, measuring defects and evaluating the condition assessme...

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months or even years. This paper investigates the performance of several soft computing techniques for point displacement modeling using GNSS technique during the process of monitoring the structural deformation of suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the availability of a large amount of positions data generated from GNSS data positions for monitoring the deformation of such structure, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS positions and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability ρ = 0.95, Δ = ± 2σ. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of ρ = 0.95, Δ = ± 2σ when using 66.67% of all data, are more accurate and reliable than any other applied methods, and therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements.

Bridges are playing a major role in the socioeconomic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months, or even years. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and points displacements prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity, and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS observations and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability ρ = 0.95, Δ = ±2σ. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of ρ = 0.95, Δ = ± 2σ when using 66.67% of all data, are more accurate and reliable than any other applied methods; therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements.

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension highway bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these structures is important and vital. One of the main objectives of monitoring the structural deformation is predicting the deformation values, which will help to avoid sudden failure and accidents in the future. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have proven successful solution in many engineering applications and problems. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and displacement prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive model for analysis of GNSS observations and displacement prediction. Several models are applied and compared for prediction of suspension bridge displacement for both kinematic and dynamic models. The resulting predicted displacement values by applying artificial neural networks (ANNs) and ANFIS provide a significant improvement for predicting the structure deformation values for suspension highway bridges from GNSS observations.

Achieving the desired results and safety for any engineering project requires regularly review and check the technical specifications and accuracy of the geodetic instruments. Standard calibration models and procedures are exist for all geodetic instruments but it must be developed and modified to meet the standard for advanced precise digital level especially for deformation measurements. Digital levels are widely used for setting out engineering structures and monitoring the structural deformation because of their accuracy, in addition to the possibility of automatically collecting and storing data, which save time and effort required for observations. When performing measurements for an industrial building or construction site, due to the operation of various mechanisms and equipment, vibration occurs on the surface of the earth or on the concrete base, on which a tripod with a digital level is mounted. Under these conditions, the frequency and amplitude of the oscillations has a great effect on the observations of modern digital geodetic instruments. This paper investigates the accuracy of precise digital level observations (height differences and distances) for two cases which are: observations in laboratory and observations in the field (open area - outdoors observations with sun light and different weather condition than laboratory). The paper presents also two new suggested observations techniques for determining the collimation error (angle (θ)) of precise levels depending on least square theory which in turn provide a significant improvement of the suggested methods for determining the characteristic of digital levels. The research presents also experimentally the results of investigating the effect of (level – tripod) vibration on the digital level observations accuracy and suggest a practical technique to reduce the influence of the tripod-level vibration system on the resulted observations.

This study presents multi input–single output (MISO) soft computing models for forecasting the significant wave height (Hs) of lakes and investigates the impact of input variables for designing the prediction models. Wind direction and speed, air and water temperature changes, wave period and direction, and sea level pressure variables are used as input parameters for the model design to predict the Hs of Michigan Lake. The M5 regression tree (M5T) and robust regression (RR) are used to study the effectiveness of input parameters that impact Hs prediction model. Furthermore, four MISO soft computing models, namely regression (RR and M5T), artificial neural network (ANN), and least square support vector machine (LSSVM), are evaluated and compared to estimate the best model for predicting the Hs. The results show that the wave’s period variable has high impact for prediction of lake Hs values, while the other variables have the same impacts on the prediction accuracy of Hs. The comparison between the developed models shows that the four model performances are acceptable in the training stage, while the MISO-ANN model gives better precision for the prediction of Hs of the lake in the testing stage.