Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE

Timely monitoring the large-scale civil structure is a tedious task demanding expert experience and significant economic resources. Towards a smart monitoring system, this study proposes a hybrid deep learning algorithm aiming for structural damage detection tasks, which not only reduces required resources, including computational complexity, data storage but also has the capability to deal with different damage levels. The technique combines the ability to capture local connectivity of Convolution Neural Network and the well-known performance in accounting for long-term dependencies of Long-Short Term Memory network, into a single end-to-end architecture using directly raw acceleration time-series without requiring any signal preprocessing step. The proposed approach is applied to a series of experimentally measured vibration data from a three-story frame and successful in providing accurate damage identification results. Furthermore, parametric studies are carried out to demonstrate the robustness of this hybrid deep learning method when facing data corrupted by random noises, which is unavoidable in reality. Keywords: structural damage detection; deep learning algorithm; vibration; sensor; signal processing.

Optimization of steel moment frames has been widely studied in the literature without considering shear deformation of panel-zones which is well-known to decrease the load-carrying capacity and increase the drift of structures. In this paper, a robust method for optimizing steel moment frames is developed in which the panel-zone design is considered by using doubler plates. The objective function is the total cost of beams, columns, and panel-zone reinforcement. The strength and serviceability constraints are evaluated by using a direct design method to capture the nonlinear inelastic behaviors of the structure. An adaptive differential evolution algorithm is developed for this optimization problem. The new algorithm is featured by a self-adaptive mutation strategy based on the p-best method to enhance the balance between global and local searches. A five-bay five-story steel moment frame subjected to several load combinations is studied to demonstrate the efficiency of the proposed method. The numerical results also show that panel-zone design should be included in the optimization process to yield more reasonable optimum designs. Keywords: direct design; differential evolution; optimization; panel-zone; steel frame.

Bài báo giới thiệu một cách tiếp cận số cho bài toán tối ưu trọng lượng dầm FGM với ràng buộc về tần số dao động riêng. Phương pháp phần tử hữu hạn dựa trên lý thuyết biến dạng trượt bậc nhất được áp dụng để phân tích dao động riêng của dầm. Luật phân bố bốn tham số và năm tham số được sử dụng để mô tả phân bố thể tích của các vật liệu thành phần trong dầm theo phương chiều cao dầm. Mục tiêu là tối ưu chiều cao dầm và phân bố vật liệu trong dầm để dầm đạt trọng lượng tối thiểu trong khi đảm bảo tần số dao động riêng cơ bản lớn hơn một giá trị cho trước. Một thuật giải tiến hóa vi phân cải tiến được xây dựng nhằm giải hiệu quả bài toán tối ưu có ràng buộc trên. Hiệu quả của phương pháp đề xuất được minh họa thông qua hai ví dụ số.

In this paper, free vibration of a functionally graded material (FGM) beam is investigated based on the Timoshenko beam theory and dynamic stiffness method. Material properties vary continuously throughout the thickness direction according to the volume fraction constituent defined by power law function. First, a consistent theory of vibration is established for FGM Timoshenko beam taking into account the actual position of neutral axis that is a useful tool for analysis of coupled vibration in the beam. Then, frequency equation obtained provides an efficient method for free vibration analysis of FGM Timoshenko beam. The theoretical development has been illustrated and validated by numerical examples. Keywords: FGM; timoshenko beam; natural frequency; dynamic stiffness method; Received: July 21th, 2016, revised: August 4th, 2016, accepted: October 13th, 2016

This research deals with the influences of macro, meso and micro steel-smooth fibers on tensile and compressive properties of strain-hardening fiber-reinforced concretes (SFCs). The different sizes, indicated by length/diameter ratio, of steel-smooth fiber added in plain matrix (Pl) were as follows: 30/0.3 for the macro (Ma), 19/0.2 for the meso (Me) and 13/0.2 for the micro fiber (Mi). All SFCs were used the same fiber volume fraction of 1.5%. The compressive specimen was cylinder-shaped with diameter × height of 150 × 200 mm, the tensile specimen was bell-shaped with effective dimensions of 25 × 50 × 100 mm (thickness × width × gauge length). Although the adding fibers in plain matrix of SFCs produced the tensile strain-hardening behaviors accompanied by multiple micro-cracks, the significances in enhancing different mechanical properties of the SFCs were different. Firstly, under both tension and compression, the macro fibers produced the best performance in terms of strength, strain capacity and toughness whereas the micro produced the worst of them. Secondly, the adding fibers in plain matrix produced more favorable influences on tensile properties than compressive properties. Thirdly, the most sensitive parameter was observed to be the tensile toughness. Finally, the correlation between tensile strength and compressive strength of the studied SFCs were also reported. Keywords: aspect ratio; strain-hardening; post-cracking; ductility; fiber size.

 A fuzzy finite element approach for static analysis of laterally loaded pile in multi-layer soil with uncertain properties is presented. The finite element (FE) formulation is established using a beam-on-two-parameter foundation model. Based on the developed FE model, uncertainty propagation of the soil parameters to the pile response is evaluated by mean of the α-cut strategy combined with a response surface based optimization technique. First order Taylor's expansion representing the pile responses is used to find the binary combinations of the fuzzy variables that result in extreme responses at an α-level. The exact values of the extreme responses are then determined by direct FE analysis at the found binary combinations of the fuzzy variables. The proposed approach is shown to be accurate and computationally efficient. Article history: Received 05 October 2017, Revised 05 March 2018, Accepted 27 April 2018

The addition of supplementary cementitious materials (SCMs) to replace cement, especially with a high volume (> 50%), is an effective way to reduce the environmental impact due to the CO2 emissions generated in the production of ultra-high performance concrete (UHPC). Unfortunately, no official guidelines of UHPC using a high volume of SCMs have been published up to now. This paper proposes a new method of mix design for UHPC using high volume fly ash (HVFA), that is referred to the particle packing optimization of the Compressive Packing Model proposed by F. de Larrard. This proposed method also considers the heat treatment curing duration to maximize the compressive strength of HVFA UHPC. The experimental results using this proposed mix design method show that the optimum fly ash content of 50 wt.% of binder can be used to produce HVFA UHPC with a compressive strength of over 120 MPa and 150 MPa under standard curing and heat treatment, respectively. Moreover, the embodied CO2 emissions of UHPC reduces 56.4% with addition of 50% FA.

This paper presents experimental results on blast testing of fortifications made from ultra high performance concrete (UHPC) and ordinary concrete (NC) by a non-contact explosion test with the TNT explosive. UHPC and NC samples used in the test were of the type of precast fortification of the real-scale and structure. TNT explosive was used in the test with a mass of 600 g per detonation. The explosive charge was centered on the top of fortifications, with the distance from the center of the explosion to the top of the fortification roof being 600 mm, 450 mm, and 300 mm, respectively. The test results, i.e., the strain of fortification roof ele-ments, the explosive load resistance, and the destruction level, were evaluated by comparing the UHPC and NC fortifications.

Paragliding is an adventure and fascinating sport of flying paragliders. Paragliders can be launched by running from a slope or by a winch force from towing vehicles, using gravity forces as the motor for the motion of flying. This motion is governed by the gravity forces as well as time-varying aerodynamic ones which depend on the states of the motion of paraglider at each instant of time. There are few published articles considering mechanical problems of paragliders in their various flying situations. This article represents the mathematical modeling and simulation of several common flying situations of a paraglider through establishing and solving the governing differential equations in state-space. Those flying situations include the ones with constant headwind/tailwind with or without constant upwind; the ones with different scenario for the variations of headwind and tailwind combined with the upwind; the ones with varying pilot mass; and the ones whose several parameters are in the form of interval quantities. The simulations were conducted using a powerful Julia toolkit called DifferentialEquations.jl. The obtained results in each situation are discussed, and some recommendations are presented. Keywords: paraglider; simulation; modeling; state-space; ordinary differential equations; Julia; DifferentialEquations.jl

This work aims at investigating the effect of aleatoric uncertainty of creep rock properties on the stability of an underground structure. This uncertainty relates to the spatial variability of the mechanical parameters representing the time-dependent behavior of geological rock formation due to the change in its mineralogy. The chosen methodology consists of representing the aleatoric uncertainty of rock properties by random fields, written as correlation functions with respect to the spatial correlation length. The adaptation of the well-known Expansion Optimal Linear Estimationmethod (EOLE) is performed to account for the cross-correlation of the random fields of the viscoplastic parameters of the host rock. Then, the Kriging-based reliability analysis is undertaken with respect to the discretized random fields, which allows elucidating the effect of spatial variability. As an application, the proposed approach is chosen to study the stability in the long-term of a deep double-lined drift within the geological disposal facilities (Cigeo project) conducted by the French National Radioactive Waste Management Agency (Andra). The drift will be excavated in Callovo-Oxfordian (COx) claystone (if the Cigeo project is licensed), considered as a potential host rock for the deep geological nuclear waste disposal in France. The results show that the chosen Kriging metamodel for the reliability analysis can be appropriate for the case of high correlation length represented by a moderate number of random variables (up to about 50) after the discretization of random fields. Further, the consideration of aleatoric uncertainty exhibits a lower probability of exceedance in comparison with the case where spatial variability is ignored. Still, more investigations need to be conducted in the future to conclude this observation.