Civil Engineering Infrastructures Journal
Volume:56 Issue: 1, Jun 2023

One of the most severe environmental problems in the world is how to dispose of waste tires properly. Many tires are dumped or thrown away worldwide every year, severely threatening the environment. Most waste tires are used as fuel by some industries, but as we know, this type of waste use has a dangerous effect. The use of synthetic fibers, especially industrial steel fibers, which is very common today and requires high raw materials and energy, negatively impacts the environment by emitting CO2 during manufacturing. Therefore, finding fibers that perform similarly to industrial steel fibers is essential. This article presents a comprehensive overview of the methods of recycling steel fibers from waste tires, the characteristics of recycled fibers, and their application in producing different cement-based composites. The effect of these recycled fibers on fresh concrete properties, including workability and porosity, has been investigated. The effect of these fibers on the concrete's mechanical characteristics, including compressive strength, splitting tensile strength, flexural strength, impact resistance, and durability, is also discussed. According to recent research, using recycled steel fibers to strengthen concrete can be a suitable alternative to industrial steel fibers, which have fewer adverse effects on the environment and reduced recycling costs.

Although the existing Driver Behavior Questionnaire (DBQ) covers a wide range of drivers' aberrant behaviors, advances in technology have made some questions out of date. These advancements could lead to human errors while driving, and therefore some items of DBQ need to be updated to reflect the influence of technology on driving behavior such as mobile phone usage while driving. This study aims to modify the widely used DBQ by including “mobile phone usage while driving” items and validate it in Iranian context. The impact of demographic items on each factor scale is also investigated. A shortened DBQ that include drivers’ aberrant behaviors and additional questions on mobile phone usage while driving was developed. A total of 298 drivers (168 males and 130 females) between the ages of 18 and 60 participated in this study. Results showed that the mean score of two mobile phone usage items is significantly correlated with violation behaviors. Besides, younger drivers, male drivers, and drivers who were involved in an accident in the past three years behave more aberrant. Statistical analysis shows that drivers who use their mobile phones while driving are more likely to be involved in a traffic crash. Moreover, mobile phone usage while driving decreases significantly by age and males use their mobile phones more than females while driving.

The basic idea of vibration-based damage identification approaches is that damage causes change in vibration response of structure. So monitoring the vibration response characteristics can be helpful in damage detection.  The main limitation in such methods is that these characteristics are also affected by the Environmental and Operational Variability (EOV) that can be incorrectly known as structural damage or sometimes cover actual damages. This paper aims to propose an innovative approach to detect and locate damage considering the EOV conditions. In this regard, an Independent Component Analysis (ICA) based Blind Source Separation (BSS) approach is employed to remove the EOV influences from the time history response of the structure. The beneficial of using the ICA-based BSS method is that there is no need to measure the environmental/operational conditions. Moreover, it is able to remove EOV influences using a limited group of response data monitored during different environmental and operational conditions. Time series analysis is then performed to extract damage-sensitive features. Finally, a statistical tool is employed to damage identification and localization by using EOV independent features. Two recognized benchmark structures are employed for verifying the accuracy of the proposed approach. Results indicate that the proposed method is a time-saving tool and efficiently successful in damage assessment of structures under EOV.

In this paper, an explicit family with higher-order of accuracy is proposed for dynamic analysis of structural and mechanical systems. By expanding the analytical amplification matrix into Taylor series, the Runge-Kutta family with  stages can be presented. The required coefficients ( ) for different stages are calculated through a solution of nonlinear algebraic equations. The contribution of the new family is the equality between its accuracy order, and the number of stages used in a single time step ( ). As a weak point, the stability of the proposed family is conditional, so that the stability domain for each of the first three orders ( 5, 6, and 7) is smaller than that for the classic fourth-order Runge-Kutta method. However, as a positive point, the accuracy of the family boosts as the order of the family increases. As another positive point, any arbitrary order of the family can be easily achieved by solving the nonlinear algebraic equations. The robustness and ability of the authors’ schemes are illustrated over several useful time integration methods, such as Newmark linear acceleration, generalized-𝛼, and explicit and implicit Runge-Kutta methods. Moreover, various numerical experiments are utilized to show higher performances of the explicit family over the other methods in accuracy and computation time. The results demonstrate the capability of the new family in analyzing nonlinear systems with many degrees of freedom. Further to this, the proposed family achieves accurate results in analyzing tall building structures, even if the structures are under realistic loads, such as ground motion loads.

Several types of steel-framed structures now require seismic retrofitting as a result of changes in their usage or modifications in seismic codes. During the last two decades, viscous dampers have been widely used for seismic rehabilitation of buildings because of their ease of application and significant reductions in structural response. The main objective of this research is to present a new comprehensive design process for seismic rehabilitation with non-linear viscous dampers and to introduce the concept of Optimal Retrofit Level (ORL) to control steel buildings. In this article, the inter-story drift as an important parameter of structural response is employed to estimate the failure cost and determine the limit state. Three-, nine- and twenty-story benchmark buildings are used to evaluate the proposed methodology. These buildings have considerably different dynamic properties. The earthquake records corresponding to three levels of seismic hazard are also applied for time-history analysis in order to investigate the trustworthiness of results obtained for zones with different seismicity. The numerical results indicate that the suggested method is able to drop lifecycle costs and creat an equilibrium between rehabilitation costs and failure costs after seismic rehabilitation.

The safety of pipe racks in petrochemical sites or refineries needs to be considered to ensure a sustainable productivity and explosions would make these structures vulnerable. Non-building structures field have remained relatively intact in comparison with ordinary buildings for which state-of-the-art guidelines are regularly proposed and existing ones are renewed. To have comprehensive knowledge on non-building structures response to blast load, multiple factors are involved. This paper pinpoints how these variables affect the pipe racks and for this purpose, ABAQUS software undertakes the solving process as multi-degree of freedom (MDOF) and Finite Element method are required. Despite ordinary buildings, pipe racks are accompanied by non-structural components whose effects in design are evaluated not significant. Moreover, adequacy and accuracy of usual analysis including static and non-linear dynamic analysis are investigated. According to the calculations, static analysis is highly sensitive to irregularities and blast duration, therefore, it may lead to invalid results. Finally, a consequence analysis is suggested to be a contribution to engineers for outlining a well-arranged layout for different sectors.

Crack detection is one of the critical tasks in health monitoring and inspection of civil engineering structures. The existence of major cracks may have detrimental effects on the integrity and performance of structures that need full consideration. Recent research into crack identification has shown an increasing interest in vision-based automated techniques, employing deep-learning computational methods such as Convolutional Neural Networks (CNNs). However, the wide range of real-world situations (e.g. camera or subject motion, misfocus, mist, and fog) can significantly compromise the accuracy of CNN-based crack identification due to a mismatched dataset in training and testing. Therefore, this study aims to establish an intelligent identification model using deep CNNs to automatically detect concrete cracks from real-world images. Moreover, the efficiency of the algorithm in identifying cracks based on blurred images in the training and validation dataset was investigated. The original dataset is replicated into various blurriness levels and split into eight different crack image sub-datasets. CNN models were trained and crack identification was carried out using different levels of image blurriness. The classification performance of the trained CNN was assessed using the concrete crack image dataset taken around Universiti Teknologi Malaysia. Sensitivity studies were also conducted to investigate the efficiency of the CNN method to identify damage under various image parameters. The results showed that the subset with the combination of sharp and slight blurriness level (blurriness Level 1) reached the highest training accuracy of 98.20%, and the network trained with blurriness Level 1 alone had the best accuracy, precision, and F1 score performance over eight training subsets. Moreover, the robustness of the networks was examined and verified under four different situations, which are; lighting, crack width, colour structures, and camera shooting angle conditions. It was observed that the presence of blurred images in the training dataset can enhance the CNN crack detection performance while high shooting angle and uneven illumination has a negative effect on the accuracy of the proposed CNN.

It is generally accepted that the shear strength of Reinforced Concrete (RC) deep beams depends on the mechanical and geometrical parameters of the beam. The accurate estimation of shear strength is a substantial problem in engineering design. However, the prediction of shear strength in this type of beams is not very accurate. One of the relatively accurate methods for estimating shear strength of beams is Artificial Intelligence (AI) methods. Adaptive Neuro-Fuzzy Inference System (ANFIS) was presented as an AI method. In this study, the efficiency of ANFIS incorporating meta-heuristic algorithms for predicting shear strength of RC beams was investigated. Meta-heuristic algorithms were used to determine the optimum parameters of ANFIS for providing the efficient models of the prediction of the RC beam shear strength. To evaluate the accuracy of the proposed method, its results were compared with those of other methods. For this purpose, the parameters of concrete compressive strength, cross-section width, effective depth, beam length, shear span-to-depth beam ratio (a/d), as well as percentage of longitudinal and transverse reinforcement were selected as input data, and the shear strength of reinforced concrete deep beam as the output data. Here, K-fold validation method with k = 10 was used to train and test the algorithms. The results showed that the proposed model with second root mean square error of 25.968 and correlation coefficient of 0.914 is more accurate than other methods. Therefore, neural fuzzy inference system with meta-heuristic algorithms can be adopted as an efficient tool in the prediction of the shear strength of deep beams.

Applications of machine learning techniques in concrete properties' prediction have great interest to many researchers worldwide. Indeed, some of the most common machine learning methods are those based on adopting boosting algorithms. A new approach, histogram-based gradient boosting, was recently introduced to the literature. It is a technique that buckets continuous feature values into discrete bins to speed up the computations and reduce memory usage. Previous studies have discussed its efficiency in various scientific disciplines to save computational time and memory. However, the algorithm's accuracy is still unclear, and its application in concrete properties estimation has not yet been considered. This paper is devoted to evaluating the capability of histogram-based gradient boosting in predicting concrete's compressive strength and comparing its accuracy to other boosting methods. Generally, the results of the study have shown that the histogram-based gradient boosting approach is capable of achieving reliable prediction of concrete compressive strength. Additionally, it showed the effects of each model's parameters on the accuracy of the estimation.

This study provides an equation of bearing capacity for a rectangular footing placed on dense sand overlying loose sand and subjected to inclined concentric loading using the limit equilibrium followed by projected area method. The parameters varied were thickness ratio (0.00 to 2.00) of the upper dense sand layer, embedment ratio (0 to 2), friction angle of upper dense (41° to 46°) sand and lower loose (31° to 36°) sand layer, and applied load inclination (0° to 30°) for the parametric study. The highest and lowest increase in the bearing capacity were observed for a friction angle combination of 46°-36° and 41°-31°, respectively, at different thickness ratios. The bearing capacity obtained from the proposed equation was approximately 4.97 and 10.5 times its initial value at embedment ratios of 1 and 2, respectively. Bearing capacity was reduced by 20.55%, 54.58% and 87.90% for load inclinations of 5°, 15°, and 30° for friction angles of upper dense and lower loose sand layer combinations of 46° and 36° and at a thickness ratio of 2. The bearing capacity obtained from the proposed equation decreased by 99.89%, 66.04%, and 61.5% as the load inclination increased from 0° to 30° for embedment ratios of 0, 1, and 2. With respect to finite element results, the average deviation of the bearing capacity obtained from the proposed equation at embedment ratios 0, 1, and 2 was 14.56%, 18.71% and 23.56%, respectively. The proposed bearing capacity equation produced results that were consistent with those reported in the literature, with an average deviation of 10.71%.

This paper presents the results from 92 fire load density surveys conducted in 52 office buildings of Pakistan. The combination method of surveying that includes both inventory and weighing methods is used to determine the fire load of 92 office rooms, including 44 private and 48 government offices. Multiple linear regression analysis techniques are applied to assess the relationship of Fire Load Density (FLD) with variables according to the characteristics of the office rooms, such as office type, category, combustible materials, room dimensions, and ventilation conditions. Probabilistic models for FLD are developed using the regression analysis of the survey data. The survey data is further used to determine the maximum fire intensity in office buildings in Pakistan. The survey results show that the FLD increases with the increase in the area of the office. The percentage of wood is found to be the most contributing factor in the fire load. It has been noted that the fire load values are different for government and private offices, whereas the Building Code of Pakistan (BCP) has the same value for both. Statistical results presented in this study will be helpful in the fire safety and fire-resistant structure design of buildings in Pakistan.

In order to obtain accurate results from displacement-based Finite Element Method (FEM), it is crucial to introduce accurate shape functions that interpolate the displacement field within an element. This paper attempts to provide such a new component by using Finite Element method using Basic Displacement Function (BDFs) for the free vibration analysis of plates with in-plane Functionally Graded Material (FGM). The first step is to introduce displacement functions and compute them using the energy method. Later, new shape functions are developed based on stiffness and force methods used to model the mechanical behavior of the element, wherein the shape functions benefit from the generality and accuracy of the stiffness and force methods. Last, the plate is analyzed using Finite Element method to derive the structural matrices from new shape functions. Several numerical examples demonstrate the accuracy and efficiency of the method, and a special material graded index named Ns is introduced.