Journal of Civil Engineering and Materials Application
Volume:7 Issue: 3, Summer 2023

Reliability is a probabilistic measure of structural safety. In Structural Reliability Analysis (SRA), both loads and resistances are modelled as probabilistic variables, and the failure of structure occurs when the total applied load is larger than the total resistance of the structure. The probability distribution of the loads as well as the resistance can depend upon multiple variables. Considering all these factors, the probability of failure of a structure is calculated.SRA can be used for systematic adjustment of structural safety factors, and for the probabilistic design and operation of structures. For example, SRA can be used to design a structure to operate during the desired lifetime safely, or it can be used for maintenance scheduling of structural systems to prevent potential failures. Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws inferences from a sample, while machine learning finds generalizable predictive patterns. ML methods can be applied to analytical and numerical SRA methods, such as First/Second-Order Reliability Methods (FORM/SORM) and First Order Second Moment (FOSM).

One of the most challenging parts of every project including prefabricated vertical drains (PVDs) combined with vacuum and surcharge preloading for ground improvement is the determination of the PVDs depth of installation and its configuration. In this paper Finite element was used for modeling and verification of a full-scale test embankment (TV2) which was constructed to study the effectiveness of PVDs combined with vacuum preloading for accelerating the consolidation along with surcharge at Bangkok airport. Different depths and scenarios were modeled and the results were compared and analyzed. Since the ultimate goal of soft clay soils treatment is attaining pre-determined settlement, the settlement curve under soil embankment was used for investigation of the results. A new Finite Element Modeling (FEM) based procedure as "One and Between Configuration" has been introduced. Based on the results, it was shown that; 1) the inward forces of vacuum preloading in proposed configuration is greater than the conventional method and lateral displacement reduced by 15 percent; 2) As a result of the lesser penetration of the mid PVD, the disturbance of the soil and accordingly, the smear zone becomes lesser; 3) Because of the "one and between" installation, in a case that a percentage of the every PVDs become clogged in any possible length, which may vary from PVD to PVD, the overall performance of the PVD itself, and in relation to adjacent PVDs don’t diminish as much as common constant penetration method.

Road engineers have always tried to enhance the tensile strength and rutting resistance of asphalt mixtures, and using various fibers is one way that helps them achieve this objective. Multi-component polymeric fibers consist of 5 components, two of which, that are quite important, are polymeric and dissolve when bitumen and materials are mixed. This research has used 0, 0.4, 0.8 and 1.2% multi-component polymeric fibers (by total weight of the mixture) to investigate and compare the performance features of asphalt mixtures containing multi-component polymeric and glass fibers. Results showed that using these fibers had positive effects, prevented main failure and highly improved the low temperature, rutting and stripping performance of asphalt mixtures.

Uniaxial compressive strength (UCS) is a critical geomechanical property of rocks that is frequently required during the preliminary stage of civil engineering design. To obtain the UCS value needs a time consuming and costly process of samples collection and preparation. There are alternate methods for determining UCS that can be conducted in situ. In this study, an attempt has been made to predict the UCS of limestone from some simple and inexpensive rock index tests such as block punch index (BPI), ultrasonic wave velocity test (Vp), Schmidt's hammer rebound number (SHR), and point load index tests (I_s50). For indirect estimation of the UCS as a function of BPI, Vp, SHR, and I_s50, block samples of limestone were collected from a quarry site in Birjand, the center of Southern Khorasan province in Iran. Then, the number of 70 core samples and 210 bulk samples were prepared and tested based on available standards. According to extensive experimental results, a database was established for estimation of the UCS via three computational methods such as support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and multi layer perceptron (MLP). After developing the models and considering several performance indices including the coefficient of determination R^2, variance account for (VAF), root mean squared error (RMSE), and using simple ranking method, the predictive models were applied to obtain the best model. Consequently, SVM approach predicted the UCS of limestone with higher accuracy in comparison to other studied computational methods.

Investigation of seismic safety evaluation of concrete dams has been the focus of many researchers due to the importance of dam safety during an earthquake. Because the destruction of these structures due to an earthquake can have negative economic and social effects. In the present study, the nonlinear dynamic analysis of gravity concrete dams has been done considering the effect of dam-reservoir interaction. In fact, the minimum and maximum principal stresses of the U-shaped dam and reservoir have been measured via ANSYS. The results show that the static analysis with non-linear behavior in the rock mass with medium and weak layers has more stability compared to the weak homogeneous system. But it is more possible to concentrate plastic strains in weak layers. Other results of this study showed that the compressive stresses in the safety check of the dam were not critical and the maximum tensile arc stresses were obtained mainly in the upper levels of the middle blocks.