It is well known that structural properties degrade under long-term environmental exposure and loading and that the degradation rate is controlled by inherent physical and chemical degradation mechanisms. The elucidation of the degradation mechanisms and the realization of effective long-term performance degradation control have been a research frontier in the field of civil engineering in recent years. Currently, the major topics that concern this research frontier include revealing the physical and chemical mechanisms of structural performance evolution under long-term environmental exposure and loading and developing structural performance degradation control technologies based on fiber-reinforced materials, for example, fiber-reinforced polymers (FRPs) and fabric-reinforced cementitious matrix (FRCM). In addition, there are novel structural performance control technologies, such as using a shape memory alloy (SMA) and self-healing concrete. This paper presents a brief state-of-the-art review of this topic, and it is expected to provide a reference for subsequent research.
Research of reliability of engineering structures has experienced a developing history for more than 90 years. However, the problem of how to resolve the global reliability of structural systems still remains open, especially the problem of the combinatorial explosion and the challenge of correlation between failure modes. Benefiting from the research of probability density evolution theory in recent years, the physics-based system reliability researches open a new way for bypassing this dilemma. The present paper introduces the theoretical foundation of probability density evolution method in view of a broad background, whereby a probability density evolution equation for probability dissipative system is deduced. In conjunction of physical equations and structural failure criteria, a general engineering reliability analysis frame is then presented. For illustrative purposes, several cases are studied which prove the value of the proposed engineering reliability analysis method.
A constrained back propagation neural network (C-BPNN) model for standard penetration test based soil liquefaction assessment with global applicability is developed, incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships. For its development and validation, a comprehensive liquefaction data set is compiled, covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries. The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints, input data selection, and computation and calibration procedures. Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model, and are thus adopted as constraints for the C-BPNN model. The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice. The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.
The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: falling weight deflectometer and ground-penetrating radar are expensive tests; back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, M5P model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.
Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R2 value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.
We propose a new algorithm, named Asymmetric Genetic Algorithm (AGA), for solving optimization problems of steel frames. The AGA consists of a developed penalty function, which helps to find the best generation of the population. The objective function is to minimize the weight of the whole steel structure under the constraint of ultimate loads defined for structural steel buildings by the American Institute of Steel Construction (AISC). Design variables are the cross-sectional areas of elements (beams and columns) that are selected from the sets of side-flange shape steel sections provided by the AISC. The finite element method (FEM) is utilized for analyzing the behavior of steel frames. A 15-storey three-bay steel planar frame is optimized by AGA in this study, which was previously optimized by algorithms such as Particle Swarm Optimization (PSO), Particle Swarm Optimizer with Passive Congregation (PSOPC), Particle Swarm Ant Colony Optimization (HPSACO), Imperialist Competitive Algorithm (ICA), and Charged System Search (CSS). The results of AGA such as total weight of the structure and number of analyses are compared with the results of these algorithms. AGA performs better in comparison to these algorithms with respect to total weight and number of analyses. In addition, five numerical examples are optimized by AGA, Genetic Algorithm (GA), and optimization modules of SAP2000, and the results of them are compared. The results show that AGA can decrease the time of analyses, the number of analyses, and the total weight of the structure. AGA decreases the total weight of regular and irregular steel frame about 11.1% and 26.4% in comparing with the optimized results of SAP2000, respectively.
This paper presents a developed meta-heuristic algorithm to optimize the shear walls of tall reinforced concrete buildings. These types of walls are considered as lateral resistant elements. In this paper, Quantum Charged System Search (QCSS) algorithm is presented as a new optimization method and used to improve the convergence capability of the original Charged System Search. The cost of tall building is taken as the objective function. Since the design of the lateral system plays a major role in the performance of the tall buildings, this paper proposes a unique computational technique that, unlike available works, focuses on structural efficiency or architectural design. This technique considers both structural and architectural requirements such as minimum structural costs, torsional effects, flexural and shear resistance, lateral deflection, openings and accessibility. The robustness of the new algorithm is demonstrated by comparing the outcomes of the QCSS with those of its standard algorithm.
A series of comprehensive parametric studies are conducted on a steel-frame structure Finite-Element (FE) model with the Multangular-Pyramid Concave Friction System (MPCFS) installed as isolators. This new introduced MPCFS system has some distinctive features when compared with conventional isolation techniques, such as increased uplift stability, improved self-centering capacity, non-resonance when subjected to near-fault earthquakes, and so on. The FE model of the MPCFS is first established and evaluated by comparison between numerical and theoretical results. The MPCFS FE model is then incorporated in a steel-frame structural model, which is subjected to three chosen earthquakes, to verify its seismic isolation. Further, parametric study with varying controlling parameters, such as isolation foundation, inclination angle, friction coefficient, and earthquake input, is carried out to extract more detailed dynamic response of the MPCFS structure. Finally, limitations of this study are discussed, and conclusions are made. The simulations testify the significant seismic isolation of the MPCFS. This indicates the MPCFS, viewed as the beneficial complementary of the existing well-established and matured isolation techniques, may be a promising tool for seismic isolation of near-fault earthquake prone zones. This verified MPCFS FE model can be incorporated in future FE analysis. The results in this research can also guide future optimal parameter design of the MPCFS.
The loading capacity in the axial direction of a bolted thin steel plate was investigated. A refined numerical model of bolt was first constructed and then validated using existing experiment results. Parametrical analysis was performed to reveal the influences of geometric parameters, including the effective depth of the cap nut, the yield strength of the steel plate, the preload of the bolt, and shear force, on the ultimate loading capacity. Then, an analytical method was proposed to predict the ultimate load of the bolted thin steel plate. Results derived using the numerical and analytical methods were compared and the results indicated that the analytical method can accurately predict the pull-through capacity of bolted thin steel plates. The work reported in this paper can provide a simplified calculation method for the loading capacity in the axial direction of a bolt.
Increasing the bending capacity of reinforced concrete (RC) elements is one of important topics in structure engineering. The goal of this study is to develop a transferred stress system (TSS) on longitudinal reinforcement bars for increasing the bending capacity of RC frames. The study is divided into two parts, i.e., experimental tests and nonlinear FE analysis. The experiments were carried out to determine the load-deflection curves and crack patterns of the ordinary and TSS fixed frame. The FE models were developed for simulating the fixed frames. The obtained load-deflection results and the observed cracks from the FE analysis and experimental tests are compared to evaluate the validation of the FE nonlinear models. Based on the validated FE models, the stress distribution on the ordinary and TSS bars were evaluated. We found the load carrying capacity and ductility of TSS fixed beam are 29.39% and 23.69% higher compared to those of the ordinary fixed beams. The crack expansion occurs on the ordinary fixed beam, although there are several crack openings at mid-span of the TSS fixed beam. The crack distribution was changed in the TSS fixed frame. The TSS fixed beam is proposed to employ in RC frame instead of ordinary RC beam for improving the performance of RC frame.
Reinforced concrete (RC) slabs are characterized by reduced construction time, versatility, and easier space partitioning. Their structural behavior is not straightforward and, specifically, punching shear strength is a current research topic. In this study an experimental database of 113 RC slabs without shear reinforcement under punching loads was compiled using data available in the literature. A sensitivity analysis of the parameters involved in the punching shear strength assessment was conducted, which highlighted the importance of the flexural reinforcement that are not typically considered for punching shear strength. After a discussion of the current international standards, a new proposed model for punching shear strength and rotation of RC slabs without shear reinforcement was discussed. It was based on a simplified load-rotation curve and new failure criteria that takes into account the flexural reinforcement effects. This experimental database was used to validate the approaches of the current international standards as well as the new proposed model. The latter proved to be a potentially useful design tool.
A method for strengthening damaged tubular steel T-joints under axial compression by wrapping them with carbon fiber-reinforced polymer (CFRP) sheets was proposed and evaluated. The influence of the CFRP strengthening on the failure mode and load capacity of T-joints with different degrees of damage was investigated using experiments and finite element analyses. Five T-joints were physically tested: one bare joint to obtain the peak load and corresponding displacement (D1m), two reinforced joints to provide a reference, and two pre-damaged then retrofitted joints to serve as the primary research objects. The ratio of the pre-loaded specimen chord displacement to the value of D1m was considered to be the degree of damage of the two retrofitted joints, and was set to 0.80 and 1.20. The results demonstrate that the maximum capacity of the retrofitted specimen was increased by 0.83%–15.06% over the corresponding unreinforced specimens. However, the capacity of the retrofitted specimen was 2.51%–22.77% lesser compared with that of the directly reinforced specimens. Next, 111 numerical analysis models (0.63≤b≤0.76, 9.70≤g≤16.92) were established to parametrically evaluate the effects of different geometric and strengthening parameters on the load capacity of strengthened tubular T-joints under different degrees of damage. The numerical analysis results revealed that the development of equivalent plastic strain at the selected measuring points was moderated by strengthening with CFRP wrapping, and indicated the optimal CFRP strengthening thickness and wrapping orientation according to tubular T-joint parameters. Finally, reasonable equations for calculating the load capacity of CFRP-strengthened joints were proposed and demonstrated to provide accurate results. The findings of this study can be used to inform improved CFRP strengthening of damaged tubular steel structures.
A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges, and in some extreme cases, it will directly lead to a bridge failure. Bridge weigh-in-motion (BWIM) system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways. However, the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge. The fast Fourier transform (FFT) method, which can significantly purify the collected structural responses (dynamic strains) received from sensors or transducers, was used in axle counting, detection, and axle weighing technology in this study. To further improve the accuracy of the BWIM system, the field-calibrated influence lines (ILs) of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method. In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones, the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system. Moreover, the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.
Landslide is a common geological hazard in reservoir areas and may cause great damage to local residents’ life and property. It is widely accepted that rainfall and periodic variation of water level are the two main factors triggering reservoir landslides. In this study, the Bazimen landslide located in the Three Gorges Reservoir (TGR) was back-analyzed as a case study. Based on the statistical features of the last 3-year monitored data and field instrumentations, the landslide susceptibility in an annual cycle and four representative periods was investigated via the deterministic and probabilistic analysis, respectively. The results indicate that the fluctuation of the reservoir water level plays a pivotal role in inducing slope failures, for the minimum stability coefficient occurs at the rapid decline period of water level. The probabilistic analysis results reveal that the initial sliding surface is the most important area influencing the occurrence of landslide, compared with other parts in the landslide. The seepage calculations from probabilistic analysis imply that rainfall is a relatively inferior factor affecting slope stability. This study aims to provide preliminary guidance on risk management and early warning in the TGR area.
This study investigates the technique of variational calculus applied to estimate the slope stability considering the mechanism of planar failure. The critical plane failure surface should be determined because it theoretically indicates the most unfavorable plane to be considered when stabilizing a slope to rectify the instability generated by several statistically possible planes. This generates integrals that can be solved by numerical methods, such as the Newton Cotes and the finite differences methods. Additionally, a system of nonlinear equations is obtained and solved. The surface of the critical planar failure is determined by applying the condition of transversality in mobile boundaries, for which various examples are provided. The number of slices is varied in one of the examples, while the surface of the critical planar failure is determined in the others. Results are compared using analytical methods through axis rotations. All the results obtained by considering normal stress, safety factors, and critical planar failure are nearly the same; however, in this research, a study is carried out for “n” number of slices using programming methods. Sub-routines are important because they can be applied in slopes with different geometry, surcharge, interstitial pressure, and pseudo-static load.
Superabsorbent Polymer (SAP) has emerged as a topic of considerable interest in recent years. The present study systematically and quantitively investigated the effect of SAP on hydration, autogenous shrinkage, mechanical properties, and microstructure of cement mortars. Influences of SAP on hydration heat and autogenous shrinkage were studied by utilizing TAM AIR technology and a non-contact autogenous shrinkage test method. Scanning Electron Microscope (SEM) was employed to assess the microstructure evolution. Although SAP decreased the peak rate of hydration heat and retarded the hydration, it significantly increased the cumulative heat, indicating SAP helps promote the hydration. Hydration promotion caused by SAP mainly occurred in the deceleration period and attenuation period. SAP can significantly mitigate the autogenous shrinkage when the content ranged from 0 to 0.5%. Microstructure characteristics showed that pores and gaps were introduced when SAP was added. The microstructure difference caused by SAP contributed to the inferior mechanical behaviors of cement mortars treated by SAP.