Showing papers in "Advances in Civil Engineering in 2020"

Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete

Abstract: The experimental design of high-strength concrete (HSC) requires deep analysis to get the target strength. In this study, machine learning approaches and artificial intelligence python-based approaches have been utilized to predict the mechanical behaviour of HSC. The data to be used in the modelling consist of several input parameters such as cement, water, fine aggregate, and coarse aggregate in combination with a superplasticizer. Empirical relation with mathematical expression has been proposed using engineering programming. The efficiency of the models is assessed by statistical analysis with the error by using MAE, RRMSE, RSE, and comparisons were made between regression models. Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. The expression tree, as well as normalization of the graph, depicts significant accuracy between target and output values. The results reveal that machine learning proposed adamant accuracy and has elucidated performance in the prediction aspect.

Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model

Abstract: Pervious concrete is an environmentally friendly material that improves water permeability, skid resistance, and sound absorption characteristics. Permeability is the most important functional performance for the pervious concrete while limited studies have been conducted to predict permeability based on mix-design parameters. This study proposed a method to combine the beetle antennae search (BAS) and random forest (RF) algorithm to predict the permeability of pervious concrete. Based on the 36 samples designed in the laboratory and 4 key influencing variables, the permeability of pervious concrete can be obtained by varying mix-design parameters by RF. BAS algorithm was used to tune the hyperparameters of RF, which were then verified by the so-called 10-fold cross-validation. Furthermore, the model to combine the BAS and RF was validated by the correlation parameters. The results showed that the hyperparameters of RF can be tuned by the BAS efficiently; the BAS can combine the conventional RF algorithm to construct the evolved model to predict the permeability of pervious concrete; the cement/aggregate ratio was the most significant variable to determine the permeability, followed by the coarse aggregate proportions.

Deep Learning-Based Safety Helmet Detection in Engineering Management Based on Convolutional Neural Networks

Abstract: Visual examination of the workplace and in-time reminder to the failure of wearing a safety helmet is of particular importance to avoid injuries of workers at the construction site. Video monitoring systems provide a large amount of unstructured image data on-site for this purpose, however, requiring a computer vision-based automatic solution for real-time detection. Although a growing body of literature has developed many deep learning-based models to detect helmet for the traffic surveillance aspect, an appropriate solution for the industry application is less discussed in view of the complex scene on the construction site. In this regard, we develop a deep learning-based method for the real-time detection of a safety helmet at the construction site. The presented method uses the SSD-MobileNet algorithm that is based on convolutional neural networks. A dataset containing 3261 images of safety helmets collected from two sources, i.e., manual capture from the video monitoring system at the workplace and open images obtained using web crawler technology, is established and released to the public. The image set is divided into a training set, validation set, and test set, with a sampling ratio of nearly 8 : 1 : 1. The experiment results demonstrate that the presented deep learning-based model using the SSD-MobileNet algorithm is capable of detecting the unsafe operation of failure of wearing a helmet at the construction site, with satisfactory accuracy and efficiency.

Thermal and Mechanical Properties of Geopolymers Exposed to High Temperature: A Literature Review

Abstract: Geopolymers are prepared by alkali solution-activated natural minerals or industrial waste materials, which have been widely used as new sustainable building and construction materials for their excellent thermal and mechanical properties. The thermal and mechanical properties of geopolymers at high temperature have attracted great attention from many researchers. However, there are few systematic works concerning these two issues. Therefore, this work reviewed the thermal and mechanical behaviors of geopolymers at high temperature. Firstly, the thermal properties of geopolymers in terms of mass loss, thermal expansion, and thermal conductivity after high temperature were explained. Secondly, the mechanical properties of residual compressive strength and stress-strain relationship of fly ash geopolymers and metakaolin geopolymers after high temperature were analyzed. Finally, the microstructure and mineralogical characteristics of geopolymers upon heating were interpreted according to the changes of microstructures and compositions. The results show that the thermal properties of geopolymers are superior to cement concrete. The geopolymers possess few mass loss and a low expansion ratio and thermal conductivity at high temperature. The thermal and mechanical properties of the geopolymers are usually closely related to the raw materials and the constituents of the geopolymers. Preparation and testing conditions can affect the mechanical properties of the geopolymers. The stress-strain curves of geopolymer are changed by the composition of geopolymers and the high temperature. The silicon-type fillers not only improve the thermal expansion of the geopolymers but also enhance mechanical properties of the geopolymers. But, they do not contribute to reducing the thermal conductivity. the different raw materials, aluminosilicate precursor and reinforcement materials, result in different geopolymer damage during the heating. However, phase transitions can occur during the process of heating regardless of the raw materials. The additional performance enhancements can be achieved by optimizing the paste formulation, adjusting the inner structure, changing the alkali type, and incorporating reinforcements.

A Novel Artificial Bee Colony Algorithm for Structural Damage Detection

Abstract: A novel artificial bee colony (ABC) algorithm to detect structural damage via modal and frequency analyses is proposed (named as TCABC algorithm). Compared to the standard ABC algorithm, tabu search method and chaotic search method are adopted in the proposed algorithm to enhance the exploration and exploitation ability. The tabu search method uses a memory function to avoid the solution being trapped in a local minimum, which increases the exploitation ability. Chaotic search method generates more searching points for finding the global minimum, which increases the exploration ability. Additionally, the first roulette wheel selection is replaced by the tournament selection to enhance the global searching ability of the TCABC algorithm. Several explicit test functions and an implicit damage detection function are employed to check the numerical results obtained from ABC and TCABC algorithms. Afterward, the damage detection accuracy of the TCABC algorithm is verified under different circumstances, and several recommendations are given for using the TCABC algorithm to detect structural damages under actual conditions. Finally, an experimental study is applied to examine the performance of TCABC algorithm for damage detection. The results show the following: (1) compared to traditional ABC algorithm, TCABC algorithm performs better; (2) fewer groups lead to faster convergence as demonstrated by both algorithms used in the same damage situation; (3) TCABC algorithm can infer the locations and extents of the damage when the groupings are inaccurate; (4) the accuracy of the field test data profoundly affects the precision of the damage detection results. In other words, stronger noises result in worse identification results; (5) whether or not the noises exist, the more data are measured, the more accurate the results can be achieved; (6) the TCABC algorithm can efficiently detect structural damage in the experimental study.

Digital Twin Hospital Buildings: An Exemplary Case Study through Continuous Lifecycle Integration

Abstract: Hospital buildings usually contain sophisticated facility systems and special medical equipment, strict security requirements, and business systems. Traditional methods such as BIM are becoming less capable of real-time updates of building status and big data volume. By proposing innovations both in technique and management—a “continuous lifecycle integration” method based on the concept of Digital Twin (DT) and “early movement” of the general contractor, this paper reported a successful project case in a large hospital in China. The case realized continuous, scheduled integration of static data and dynamic data of more than 20 management systems from the design, construction, pre-O&M phase up to the O&M phase. Then, a DT software system with real-time visual management and artificial intelligent diagnosis modules was developed and deployed in a newly built DT control center. Managers have the ability to grasp the detailed status of the whole hospital by visual management and receive timely facility diagnosis and operation suggestions that are automatically sent back from the digital building to reality. The case has been steadily running for more than a year in the hospital and achieved desired performance by reducing energy consumption, avoiding facility faults, reducing the number of requested repairs, and enhancing the quality of daily maintenance work.

Shear Behaviour of RC Beams Strengthened by Various Ultrahigh Performance Fibre-Reinforced Concrete Systems

Abstract: This study presents a numerical investigation on the shear behaviour of shear-strengthened reinforced concrete (RC) beams by using various ultrahigh performance fibre-reinforced concrete (UHPFRC) systems. The proposed 3D finite element model (FEM) was verified by comparing its results with those of experimental studies in the literature. The validated numerical model is used to analyse the crucial parameters, which are mainly related to the design of RC beams and shear-strengthened UHPFRC layers, such as the effect of shear span-to-depth ratio on the shear behaviour of the strengthened or nonstrengthened RC beams and the effect of geometry and length of UHPFRC layers. Moreover, the effect of the UHPFRC layers’ reinforcement ratio and strengthening of one longitudinal vertical face on the mechanical performance of RC beams strengthened in shear with UHPFRC layers is investigated. Results of the analysed beams show that the shear span-to-depth ratio significantly affects the shear behaviour of not only the normal-strength RC beams but also the RC beams strengthened with UHPFRC layers. However, the effect of shear span-to-depth ratio has not been considered in existing design code equations. Consequently, this study suggests two formulas to estimate the shear strength of normal-strength RC beams and UHPFRC-strengthened RC beams considering the effect of the shear span-to-depth ratio.

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Abstract: The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

Improving Effectiveness of Safety Training at Construction Worksite Using 3D BIM Simulation

Abstract: Construction worker safety and safety training continue to be the main issues in the construction industry. As a means of improving construction worker safety, this study focuses on safety training at an actual construction worksite. In order to promote safety awareness among workers, it is imperative to develop more effective safety training. This study examined safety training as a method of improving construction worker safety, focusing on the effectiveness of the instructional delivery method. Effectiveness pertains to level of understanding of instruction and can be enhanced through improving instructional delivery method. This study aims to examine two different types of safety training methods: (1) the conventional lecture method and (2) innovative method using the 3D Building Information Modeling (BIM) simulation, reflecting the hazard condition of the actual site. An experiment is conducted, in which the two types of training are implemented and assessed through testing trainees’ understanding. The workers trained via BIM simulation showed a higher level of understanding than the group of workers who were trained conventionally. Also, a survey was conducted targeting safety managers, in which the workers evaluated lifelike quality of the training, active learning and enjoyment that each of the training methods can promote. This research will provide implications that an innovative method using the virtual reality is more effective than the conventional lecture method.

Failure Evolution Law of Reinforced Anchor System under Pullout Load Based on DIC

Abstract: To obtain the failure evolution law, a pullout test model of the anchor system is proposed based on the digital image correlation (DIC) measurements. By the study of the displacement field, the strain field, and the force transfer law of the anchor system under the pulling load, the failure law of the anchor system is revealed. The results show that (1) the failure mode and the ultimate bearing capacity of the anchor system are related to the thickness of the anchor agent; (2) in the anchor system, the pulling force is gradually transferred from the loading end to the free end along the steel bar, and the greater the thickness of the anchoring agent, the deeper the transfer range; (3) during the loading, the deformation of the anchoring system is mainly concentrated at the interface between the anchoring agent and the concrete and expands to the depth along the steel bar; and (4) the failure evolution rate of the anchorage system is related to the loading stage. The failure evolution of the anchor system can be divided into the elastic phase, the plastic phase, and the deformation rebound phase.

Improved Nonlinear Nishihara Shear Creep Model with Variable Parameters for Rock-Like Materials

Abstract: Creep property is an important mechanical property of rocks. Given the complexity of rock masses, mechanical parameters change with time in the creep process. In this work, a nonlinear function for describing the time-dependent change of parameters was introduced and an improved variable-parameter nonlinear Nishihara shear creep model of rocks was established. By creating rock-like materials, the mechanical properties of rocks under the shear creep test condition were studied, and the deformation characteristics and long-term shear strength of rocks during creep were analyzed. The material parameters of the model were identified using the creep test results. Comparison of the model’s calculated values and experimental data indicated that the model can describe the creep characteristics of rocks well, thus proving the correctness and rationality of the improved model. During shear creep, the mechanical properties of rocks have an aging effect and show hardening characteristics under low shear stress. Furthermore, according to the fact that Gk of the nonlinear model can characterize the creep deformation resistance, a method to determine the long-term shear strength is proposed.

Optimization Analysis of Smart Steel-Plastic Geogrid Support for Tunnel

Abstract: With the concept of smart geogrid coming out, many scholars have built optical fiber into the geogrid to form a kind of smart geogrid material with self-sensing function of structural deformation. It can not only reinforce the parts with potential safety hazards, but also have the functions of safety monitoring, intelligent prevention, and control of engineering disasters, which is of great significance for ensuring the safety of tunnel construction and improving the tunnel monitoring methods. Based on predecessors’ research on smart geogrid tensile calibration experiment and sensor method simulation and experimental verification, this paper analyzes the smart geogrid and the tunnel surrounding rock as a whole, to study the deformation coordination mechanism between the geogrid material and the tunnel surrounding rock. Referring to the relevant engineering practice case, through finite element numerical simulation, the optimal layout of smart geogrid material was explored, and the principle of discrete curvature reconstruction curve sensing of smart geogrid was optimized by simulating the working conditions of different construction methods and supporting conditions, in order to provide a theoretical basis for the application of smart geogrid material in practical tunnel engineering.

Crack Initiation Behaviors of Granite Specimens Containing Crossing-Double-Flaws with Different Lengths under Uniaxial Loading

Abstract: Crack initiation is an important stage in the failure process of rock masses. In this paper, crack initiation behaviors (crack initiation model, crack initiation location, crack initiation angle, and crack initiation stress) of granite specimens containing crossing-double-flaws with different lengths were investigated using PFC2D software. Crack initiation models were all tensile wing cracks, which did not exactly initiate from the main flaw with a length of 30 mm. They can initiate from the secondary flaw with a length 20 mm at of 30° (included angle between main flaw and horizontal direction) and of 90° (included angle between main and secondary flaws) and from main and secondary flaws at of 30° and of 60°. These were mainly induced by the superposition of stress fields around the main and secondary flaws as varied from 0° to 90°, especially the tensile force concentration zones superposition. The tensile forces concentration zone around flaw shrank towards flaw tips with the increase of flaw’s inclinations measured horizontally. Under stress field superposition effects, the crack initiation stress decreased firstly and then increased with at of 30° and 45°. Crack initiation locations were close to flaw tips but not restricted to them. The distances between crack initiation locations and flaw tips, and the crack initiation angles depended on the flaw where first macrocracks initiated from. Microdisplacement field distributions of granite specimens to reveal the mesomechanism of crack initiation behaviors were discussed.

Vision-Based Three-Dimensional Reconstruction and Monitoring of Large-Scale Steel Tubular Structures

Abstract: A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST; then point cloud acquisition, point cloud filtering, and point cloud stitching algorithms are applied to obtain a 3D point cloud of the specimen surface. A point cloud correction algorithm based on geometric features and a deep learning algorithm are utilized, respectively, to correct the coordinates of the stitched point cloud. This enhances the vision measurement accuracy in complex environments and therefore yields a higher-accuracy 3D model for the purposes of real-time complex surface monitoring. The performance indicators of the two algorithms are evaluated on actual tasks. The cross-sectional diameters at specific heights in the reconstructed models are calculated and compared against laser rangefinder data to test the performance of the proposed algorithms. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after correction and meets the requirements for dynamic monitoring. The proposed methodology is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features.

Tunnelling-Induced Settlement and Treatment Techniques for a Loess Metro in Xi’an

Abstract: Techniques including pre-grouting, long pipe roof, and parameter optimization were employed to ensure the safety of loess metro tunnelling under an existing glass building. Their effects were proved through monitoring the settlement of building and surface during tunnelling. Besides, division of settlement monitoring according to processes, a new method, was conducted to control settlement in time. The highest surface settlement after construction was 16 mm only, meeting the requirement. The result indicates that it is practicable to control the tunnelling settlement strictly in extremely difficult geological areas. The settlement regularities were also studied through numerical simulation; their deformation is larger compared with in situ results while their change trends coincide during most processes. Soil excavations cause settlement primarily, accounting for more than 60%. It is suggested that dual slurry pre-grouting and process-based measurement should be employed before each excavation in water-rich loess areas.

Shallow Foundation Settlement Quantification : Application of Hybridized Adaptive Neuro-Fuzzy Inference System Model

Abstract: Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.

Evaluation of Design and Simulation of Creep Test Rig for Full-Scale Crossarm Structure

Abstract: A simulated model was developed in order to design and simulate the mechanical properties of a cantilever beam creep testing rig for a full-scale size crossarm in transmission towers. Currently, the Malaysian power grid system is implementing several materials, such as Chengal wood, polymeric composite, and galvanised steel, as crossarm structures. However, there is a lack of study regarding the long-term mechanical behaviour of heavy structures in the literature. Hence, this article explains the design development of creep test rig for a full-scale crossarm structure using CATIA and mechanical simulation (deformation and safety factors) of the product via ANSYS. The test rig will be used to predict the creep life of the cantilever beam structure. In this study, a tall and large base area structure was designed and replicated from an actual tower to elevate the crossarm above the ground level. In order to select the best performance model, a baseline conceptual test rig was generated in CAD modelling, and the finite element analysis was carried out by using a static structural analysis in ANSYS. Four different bracing configurations were incorporated in the baseline model, and the modified structures were then analysed. The results show that the hybrid bracing configuration has enhanced the mechanical properties and safety factors in the baseline model.

Automatic Monitoring System in Underground Engineering Construction: Review and Prospect

Abstract: Automatic monitoring system is one of the main means to ensure the safety of underground engineering construction This paper summarizes the current international research and application status of the underground engineering monitoring system from three aspects of data acquisition, data transmission, and data processing and emphatically introduces the mainstream new technology of the monitoring system Furthermore, this paper puts forward specific and implementable technical routes based on the current intelligent technology and the challenges faced by future monitoring, which can provide direction and reference for future research, including high-precision real-time acquisition and safe and reliable transmission of monitoring data, multisource data fusion, and the visual intelligent early warning platform

Anchor Stress and Deformation of the Bolted Joint under Shearing

Abstract: Bolts are widely used in rock mass engineering, wherein the bolt support improves the safety and stability of the rock mass. To reveal the mechanical behavior of the bolt and failure mechanism of the bolted joint in the shearing process, a direct shear test was conducted by changing the state of grouting, number of bolt, and inclination angle of the bolt. The change in the axial force of the anchor in the shearing process was evaluated by conducting a strain gauge test, and the mechanical behavior of the bolt under the external force was studied. The results showed that under the same normal stress, the yield displacement of the bolt decreased and the stiffness of the joint gradually increased with increased number of bolts. At the same number of bolts, their yield displacement increased with increased normal stress. Analysis further revealed that grouting on the joint improved the force condition of the bolt, increased the yield displacement of the bolt, and coordinated the deformation of the grouting body and bolt, thereby improving the shear strength of the joint. Lastly, when the anchor angles differed, the axial pulling resistance of the anchor changed, and the yield displacement of the anchor with 45° inclination was <90°. The yield displacement of the bolt showed that the supporting effect of the bolt with a 45° inclination was better than that of the bolt with a 90° inclination.

Fresh and Hardened Properties of Concrete Incorporating Binary Blend of Metakaolin and Ground Granulated Blast Furnace Slag as Supplementary Cementitious Material

Abstract: +e growing demand for cement has created a significant impact on the environment. Cement production requires huge energy consumptions; however, Pakistan is currently facing a severe energy crisis. Researchers are therefore engaged with the introduction of agricultural/industrial waste materials with cementitious properties to reduce not only cement production but also energy consumption, as well as helping protect the environment. +is research aims to investigate the influence of binary cementitious material (BCM) on fresh and hardened concrete mixes prepared with metakaolin (MK) and ground granulated blast furnace slag (GGBFS) as a partial replacement of cement. +e replacement proportions of BCM used were 0%, 5%, 10%, 15%, and 20% by weight of cement. A total of five mixes were prepared with 1 : 1.5 : 3 mix proportion at 0.54 water-cement ratios. A total of 255 concrete specimens were prepared to investigate the compressive, tensile, and flexural strength of concrete after 7, 28, and 56 days, respectively. It was perceived that the workability of concrete mixes decreased with an increasing percentage of MK and GGBFS. Also, the density and permeability of concrete decreased with an increasing quantity of BCM after 28 days. Conversely, the compressive, tensile, and flexural strength of concrete were enhanced by 12.28%, 9.33%, and 9.93%, respectively, at 10% of BCM after 28 days. +e carbonation depth reduced with a rise in content of BCM (up to 10%) and then later improved after 28, 90, and 180 days. Moreover, the effect of chloride attack in concrete is reduced with the inclusion of BCM after 28 and 90 days. Similarly, the drying shrinkage of concrete decreased with an increase in the content of BCM after 40 days.

Experimental Investigation of Ground and Air Temperature Fields of a Cold-Region Road Tunnel in NW China

Abstract: To fully understand the temperature distribution of cold regions and the variation law of temperature fields in cold-region tunnels, this paper presents a case-history study on a tunnel located on the eastern Qinghai-Tibet Plateau, China. The conclusion is as follows: the temperature outside the tunnel and the ambient temperature are affected by wind speed and light. The law of the temperature field in the tunnel is greatly affected by wind speed and wind direction. According to the field test, the wind speed in the tunnel is about 2.8 m/s in winter, and the daily average temperature at the exit of the tunnel is basically lower than that at the entrance. From the central to the entrance, the temperature in the tunnel decreases by 0.11°C every 10 meters along the longitudinal direction; from the central to the exit, the temperature in the tunnel increases by 0.07°C every 10 meters. In this regard, for the problems of lining frost damage and central drainage pipe freezing, it is suggested to adopt the way of heating and drainage, but heating the freezing area outside the drainage pipe should be avoided. The test results can provide references for the design, construction, and research of the temperature field of the tunnel antifreezing system in the cold region. It is hoped that the test results can be useful in the design and construction of frost damage prevention systems and the investigation of temperature fields in cold-region tunnels.

Physical and Chemical Properties, Pretreatment, and Recycling of Municipal Solid Waste Incineration Fly Ash and Bottom Ash for Highway Engineering: A Literature Review

Abstract: Municipal solid waste incineration (MSWI) has been widely used due to its benefits in reducing waste and recovering energy. However, MSWI fly ash and bottom ash are increasing rapidly, causing harm to human health and the environment. This paper discussed the production process, physical and chemical properties, leaching properties, pretreatment methods, and applications of fly ash and bottom ash. By summarizing the previous literature, it is found that MSWI fly ash and bottom ash have mechanical properties similar to natural aggregate. Many beneficial attempts have been made in cement concrete aggregates, ceramic raw materials, and highway engineering materials. Due to concerns about the leaching of heavy metals in fly ash, its application in highway engineering is limited. The application of bottom ash in asphalt pavement is rare because of the side effect on the performance of asphalt mixture. Considering the solidification effect of cement on heavy metals and the low cost of fly ash and bottom ash, the application in cement-stabilized macadam base has broad application prospects. This is beneficial to reduce the construction cost and promote the process of waste incineration, especially in developing countries.

Risk identification and responses of tunnel construction management during the COVID-19 pandemic

Abstract: The COVID-19 pandemic has brought about significant influences to the world, including tunnel construction Based on the analysis of 12 tunnel construction projects, this paper identifies the specific risk factors related to the COVID-19 pandemic, e g , men, materials, machines, methods, social environment, and political epidemic prevention pressure Among these risk factors, worker availability, site accessibility, shortage of construction materials, and inadequate epidemic prevention materials caused by the lockdown policy are the most fundamental challenges encountered by the projects Social panic and epidemic prevention policy requirements are key issues needed to be addressed before the resumption of construction work The special circumstances caused by the COVID-19 pandemic called for flexible project management and coordination skills to raise suitable and effective response strategies, while local governments make substantial contributions in solving the difficulties Although these measures have resulted in higher project costs, their effectiveness in catching up with project schedules is worthy of recognition The findings of this study enrich the risk categories of tunnel construction and the risk response strategies from the perspective of a global pandemic It implies that future construction schemes including design, budget, supply chain, and project management should consider the possible influence of an epidemic © 2020 Zhimin Wang et al

Investigation of Moisture Sensitivity and Conductivity Properties of Inductive Asphalt Mixtures Containing Steel Wool Fiber

Abstract: The construction of suitable roads in rainy areas has created problems in the construction process due to the low resistance of asphalt to moisture. To solve this problem, materials are commonly used that make mixtures resistant to moisture; however, these materials may reduce the dynamic resistance of asphalt. Therefore, materials should be used that, in addition to increasing the dynamic resistance, also increase the moisture resistance of asphalt mixtures. One of these materials used in this research is steel wool fiber (SWF), which in addition to creating conductive roads also could have a significant effect on moisture resistance. In this study, the impact of 2%, 4%, 6%, 8%, and 10% SWF on the Marshall stability and moisture sensitivity of mixtures was investigated using the Marshall stability and indirect tensile strength (ITS) tests, respectively. Moreover, using SWF as a conductive fiber, the conductivity properties of asphalt mixtures were explored to find the optimal amount of electrical conductivity. The results of the Marshall stability test indicated that by increasing SWF contents, the stability of mixtures increased, compared with the base sample, and greater amounts of 6% SWF resulted in the reduction of the Marshall stability. The results of ITS showed that modification of bitumen by SWF increased ITS and tensile strength ratio (TSR) amounts of mixtures. 6% SWF was the optimal amount for enhancing the resistance of asphalt mixtures to moisture sensitivity. The results of the electrical resistivity test showed that the resistivity had three phases: high resistivity, transit, and low resistivity. Mixtures containing less than 4% SWF illustrated an insulating behavior, with electrical resistivity greater than 7.62 108 . At the transit phase, the resistivity of mixtures had a sharp reduction from 7.62 108 to 6.17 104 . Finally, 8% SWF was known as the optimal content for the electrical conductivity of mixtures.

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Abstract: The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill

Abstract: Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining.

A Review of the Engineering Properties of Metakaolin Based Concrete: Towards Combatting Chloride Attack in Coastal/Marine Structures

Abstract: Changing human lifestyle and increasing urbanisation are contributory factors to the high demand for concrete construction materials across the globe. With the imminent developments in the unpopulated marine/coastal zones, higher installation of concrete facilities is still expected. However, poor design and construction procedures coupled with inadequate materials selection and exposure to aggressive environmental conditions, such as chloride laden environments, often result in the reduced aesthetic and structural performance of concrete. Deterioration of reinforced concrete structures located in a coastal/marine setting can influence the safety, economic, and sustainability aspects of society. Hence, there is an increased need for alternate binder systems with the ability to reduce the effects of chloride attack in concrete. 1is paper presents a critical review of the engineering properties of metakaolin (MK) based concrete exposed to chloride attack. 1e key advantages and limitations of using MK for concrete production purposes were outlined and evaluated. Areas for future research were also highlighted in this paper. Based on the favourable 28-day compressive strength (73–84 MPa) and durability performance documented across the numerous past year studies that were reviewed, it can be concluded that MK is a viable alternate binder material for combatting chloride attack in coastal/marine concrete structures. 1is, in conjunction with its lack of chemical CO2 emissions, proves that MK can be used to improve the serviceability and sustainability states of marine structures. 1e viewpoint of this review will guide concrete constructors and researchers on a possible framework for the utilisation of metakaolin for enhancing durability concrete in aggressive environments.

Improvement of Grid Independence Test for Computational Fluid Dynamics Model of Building Based on Grid Resolution

Abstract: Computational fluid dynamics (CFD) is being used in various research fields on the building environment. Target space of the CFD model is divided into a finite number of grids for numerical analysis. Therefore, an optimal grid design is required to obtain accurate results. The grid independence test is generally performed to design an optimal grid. However, given that there is no standardized procedure for gird independence test, most depend on the researcher’s experience and knowledge. In the conventional method, the subjective judgment of the researcher affected the selection of the grid conditions and the criteria for the optimal grid. It can lead to a decrease in the reliability of the simulation results by poor grid design. This study proposed a grid independence test method that applies the grid resolution to improve the conventional method. The grid resolution was calculated by applying the characteristic length. CV(RMSE) and R2 were applied as the criteria for optimal grid. A case study was conducted to evaluate the adequacy of the proposed method. As the characteristic length increased, the optimal grid resolution increased. In particular, for a characteristic length of 0.7 or more, the optimal grid resolution was evaluated as 24. The grid convergence index (GCI) was calculated to verify the suitability of the proposed method. As a result, all of the optimal grid resolution derived from the proposed method was evaluated as the optimal condition.

Importance-Performance Analysis of Prefabricated Building Sustainability: A Case Study of Guangzhou

Abstract: In the era of sustainability as the development concept, prefabricated buildings have gradually become an important way to achieve sustainable development of the construction process due to the advantages of high construction speed, energy-saving, and environmental protection. In order to make the prefabricated building develop in a sustainable direction, it is necessary to understand the importance and performance of the critical sustainability aspects of the prefabricated building. However, the existing research has not fully explored this point, and classification research on all aspects of sustainability according to the management priorities of sustainable development is lacking. The present study determines the critical sustainability characterization items (criteria) of prefabricated buildings and uses the importance-performance analysis (IPA) method to explore the sustainability importance and performance level of prefabricated buildings in Guangzhou on the basis of the three dimensions of economic, social, and ecological sustainability. In particular, this study revises the traditional IPA method and uses the comprehensive weight obtained by the analytical network process- (ANP-) entropy weight method to obtain the importance of items. Results show that items “environmental protection” and “construction civilization” are of high importance and perform well. “Construction cost” and “product quality” are considered high-importance items with relatively poor performance; that is, these areas require urgent improvement actions. The “policy support” item at the intersection of IPA coordinates is also an aspect worthy of attention and discussion. This study provides a useful reference for decision-makers and relevant personnel on determining the priority of project management and achieving the optimal allocation of resources to promote the sustainable development of prefabricated buildings.

Life-Cycle Assessment of Recycling Postconsumer High-Density Polyethylene and Polyethylene Terephthalate.

Abstract: This study aims to quantify the overall environmental performances of mechanical recycling of the postconsumer high-density polyethylene (HDPE) and polyethylene terephthalate (PET) in Jordan. The life-cycle assessment (LCA) methodology is used to assess the potential environmental impacts of recycling postconsumer PET and HDPE. It quantifies the total energy requirements, energy sources, atmospheric pollutants, waterborne pollutants, and solid waste resulting from the production of recycled PET and HDPE resin from the postconsumer plastic. System expansion and cut-off recycling allocation methods are applied. The analysis has been carried out according to the LCA standard, series UNI EN ISO 14040-14044:2006. A standard unit of output (functional unit) is defined as “one ton of PET flake” and “one ton of HDPE pellet.” The results of the production of virgin resin are compared with the “cut-off” and “system expansion” recycling results. Depending on the allocation methods applied, a nonrenewable energy saving of 40–85% and greenhouse gas emission saving of 25–75% can be achieved. Based on two allocation methods, PET and HDPE recycling offers important environmental benefits over single-use virgin PET and HDPE. LCA offers a powerful tool for assisting companies and policy-makers in the waste plastic industry. Furthermore, the “system expansion” recycling method is not easy to apply because it requires detailed data outside of the life cycle of the investigated product.