• Earthquakes and Structures
• /
• v.20 no.1
• /
• pp.97-107
• /
• 2021

A comprehensive study on seismic performance of wood frame building in hilly regions is presented. Specifically, seismic fragility assessment of a typical wood frame building at various locations of the northeast region of India are demonstrated. A three-dimensional simplified model of the wood frame building is developed with due consideration to nonlinear behaviour of shear walls under lateral loads. In doing so, a trilinear model having improved capability to capture the force-deformation behaviour of shear walls including the strength degradation at higher deformations is proposed. The improved capability of the proposed model to capture the force-deformation behaviour of shear wall is validated by comparing with the existing experimental results. The structural demand values are obtained from nonlinear time history analysis (NLTHA) of the three-dimensional wood frame model considering the effect of uncertainty due to record to record variation of ground motions and structural parameters as well. The ground motion bins necessary for NLTHA are prepared based on the identified hazard level from probabilistic seismic hazard analysis of the considered locations. The maximum likelihood estimates of the lognormal fragility parameters are obtained from the observed failure cases and the seismic fragilities corresponding to different locations are estimated accordingly. The results of the numerical study show that the wood frame constructions commonly found in the region are likely to suffer minor cracking or damage in the shear walls under the earthquake occurrence corresponding to the estimated seismic hazard level; however, poses negligible risk against complete collapse of such structures.

• Structural Engineering and Mechanics
• /
• v.83 no.3
• /
• pp.401-413
• /
• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Structural Monitoring and Maintenance
• /
• v.8 no.4
• /
• pp.379-402
• /
• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.583-599
• /
• 2022

In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

• Structural Engineering and Mechanics
• /
• v.85 no.3
• /
• pp.369-379
• /
• 2023

Reliability-Based Design Optimization (RBDO) is an appropriate framework for obtaining optimal designs by taking uncertainties into account. Large-scale problems with implicit limit state functions and problems with discrete design variables are two significant challenges to traditional RBDO methods. To overcome these challenges, this paper proposes a hybrid method to perform RBDO of structures that links Firefly Algorithm (FA) as an optimization tool to advanced (finite element) reliability methods. Furthermore, the Genetic Algorithm (GA) and the FA are compared based on the design cost (objective function) they achieve. In the proposed method, Weighted Simulation Method (WSM) is utilized to assess reliability constraints in the RBDO problems with explicit limit state functions. WSM is selected to reduce computational costs. To performing RBDO of structures with finite element modeling and implicit limit state functions, a First-Order Reliability Method (FORM) based on the Direct Differentiation Method (DDM) is utilized. Four numerical examples are considered to assess the effectiveness of the proposed method. The findings illustrate that the proposed RBDO method is applicable and efficient for RBDO problems with discrete and continuous design variables and finite element modeling.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.617-624
• /
• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.

• Geomechanics and Engineering
• /
• v.28 no.1
• /
• pp.65-75
• /
• 2022

Time and cost of construction are key factors in decision-making during a tunnel project's planning and design phase. Estimations of time and cost of tunnel construction projects are subject to significant uncertainties caused by uncertain geotechnical and geological conditions. The Gaussian Process Regression (GPR) technique for predicting ground condition and construction time and cost of mountain tunnel projects is used in this work. The GPR model is trained with data from past mountain tunnel projects. The model is applied to a case study in which the predicted time and cost of tunnel construction using the GPR model are compared with the actual construction time and cost for model validation and reducing the uncertainty for the future projects. In addition, the results obtained from the GPR have been compared with to other models of artificial neural network (ANN) and support vector regression (SVR) that the GPR model provides more accurate results.

• Structural Engineering and Mechanics
• /
• v.81 no.4
• /
• pp.429-441
• /
• 2022

A probabilistic assessment of the seismic-excited buildings with a multiple-tuned-mass-damper (MTMD) system is carried out in the presence of uncertainties of the structural model, MTMD system, and the stochastic model of the seismic excitations. A free search optimization procedure of the individual mass, stiffness and, damping parameters of the MTMD system based on the snap-drift cuckoo search (SDCS) optimization algorithm is proposed for the optimal design of the MTMD system. Considering a 10-story structure in three cases equipped with single tuned mass damper (STMS), 5-TMD and 10-TMD, sensitivity analyses are carried out using Sobol' indices based on the Monte Carlo simulation (MCS) method. Considering different seismic performance levels, the reliability analyses are done using MCS and kriging-based MCS methods. The results show the maximum structural responses are more affected by changes in the PGA and the stiffness coefficients of the structural floors and TMDs. The results indicate the kriging-based MCS method can estimate the accurate amount of failure probability by spending less time than the MCS. The results also show the MTMD gives a significant reduction in the structural failure probability. The effect of the MTMD on the reduction of the failure probability is remarkable in the performance levels of life safety and collapse prevention. The maximum drift of floors may be reduced for the nominal structural system by increasing the TMDs, however, the complexity of the MTMD model and increasing its corresponding uncertainty sources can be caused a slight increase in the failure probability of the structure.

• Earthquakes and Structures
• /
• v.21 no.5
• /
• pp.505-514
• /
• 2021

A formulation based on structural reliability and cost effectiveness is proposed to provide recommendations to select the best retrofit strategy for schools with reinforced concrete frames and masonry walls, among three proposed alternatives. The cost calculation includes the retrofit cost and the expected costs of failure consequences. Also, the uncertainty of the seismic hazard is considered for each school site. The formulation identifies the potential failure modes, among shear and bending forces for beams, and flexure-compression forces for columns, for each school, and the seismic damages suffered by the schools after the earthquake of September 17, 2017 are taken into account to calibrate the damaged conditions per school. The school safety level is measured through its global failure probability, instead of only the local failure probability. The proposed retrofit alternatives are appraised in terms of the cost/benefit balance under future earthquakes, for the respective site seismic hazard, as opposed to the current practice of just restoring the structure original resistance. The best retrofit is the one that corresponds to the minimum value of the expected life cycle cost. The study, with further developments, may be used to develop general recommendations to retrofit schools located at seismic zones.

• Geomechanics and Engineering
• /
• v.32 no.3
• /
• pp.271-291
• /
• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.