Abstracts:During the operation of metro tunnels, structural performance could inevitably degrade due to the combined effects of the stochastic and disadvantageous environment. In order to reduce the randomness and uncertainty underlying the structural safety risk analysis in operational tunnels, this paper develops a novel hybrid approach to perform global sensitivity analysis. The deterministic and stochastic finite element (FE) model is used to develop the approximate relationship between input and output parameters with a high level of accuracy. Based on the simulated data from an FE model, a meta-model is constructed by a built Particle Swarm Optimization-Least Square Support Vector Machine (PSO-LSSVM) model. In this research, 10,000 groups of data are generated by the built PSO-LSSVM model, which provides data support for the global sensitivity analysis through Extended Fourier Amplitude Sensitivity Test (EFAST). The input variables with a high global sensitivity are identified as crucial variables which should be well controlled and managed during tunnel operation. A Hankou-Fanhu (H-F) tunnel section in the Wuhan metro system is utilized as a case study to verify the applicability of the proposed approach. Global sensitivity analysis enables the reduction of the epistemic uncertainty in tunnel structural safety management, providing insight into a better understanding of (1) the input-output causal relationships of the structural safety risk in operational tunnels, (2) the reduction of the epistemic uncertainty in project safety management of operational tunnels.

Abstracts:Since the 1980s, Terrestrial laser scanning is successfully adopted in geodesy for contact-free measurements. Collecting dense point-clouds by using TLS is proven as increasingly useful in several other quasi-geodetic, structural, and civil engineering applications. In the study, the newest trend of harnessing TLS is discussed in association with assessing the properties of a scanned object as opposed to its geometrical location. The most promising area of the aforementioned application of TLS is moisture detection in buildings and structures. The present study involved a thorough research programme dedicated to this topic as described in previous publications. Different scanners utilizing visible green and infrared laser beam were harnessed in the research programme. Such aspects of scanning porous construction materials as roughness, colour and presence of water are analysed. Based on the experience, the possibilities and limitations of harnessing TLS for moisture detection in building materials are discussed in the study.

Abstracts:The field of Civil Engineering has lately gained increasing interest in Unmanned Aerial Vehicles (UAV), commonly referred to as drones. Due to an increase of deteriorating bridges according to the report released by the American Society of Civil Engineers (ASCE), a more efficient and cost-effective alternative for bridge inspection is required. The goal of this paper was to analyze the effectiveness of drones as supplemental bridge inspection tools. In pursuit of this goal, the selected bridge for inspection was a three-span glued-laminated timber girder with a composite concrete deck located near the city of Keystone in the state of South Dakota (SD). A drone, a Dà-Jiāng Innovations (DJI) Phantom 4, was utilized for this study. Also, an extensive literature review to gain knowledge on current bridge inspection techniques using drones was conducted. The findings from the literature review served as the basis for the development of a five-stage drone-enabled bridge inspection methodology. A field inspection utilizing the drone was performed following the stages of the methodology, and the findings were compared to current historical inspection reports provided by the SD Department of Transportation (SDDOT). Quantified data using the drone such as a spalled area of 0.18 m², which is identical to the measurement provided by the SDDOT (0.3 m by 0.6 m), demonstrated the efficiency of the drone to inspect the bridge. This study detailed drone-enabled inspection principles and relevant considerations to obtain optimum data acquisition. The field investigation of the bridge demonstrated the image quality and damage identification capabilities of the drone to perform bridge inspection at a lower cost when compared to traditional methods.

Abstracts:Conflicts in construction projects have always been a major problem. Unless an alternate resolution mechanism is spelled out in the contract, these disputes are typically resolved in court, which might be time consuming and financially substantial. This paper represents a continuation in a research focused on creating robust methodologies for legal decision support within the construction industry. Consequently, this papers tackles the problem of automating the extraction of implicit knowledge about significant legal factors upon which verdicts of Differing Site Condition (DSC) litigations are based. To that end, the research methodology (1) utilized a set of 600 cases from the Federal Court of New York; (2) adopted 15 legal concepts that have been found to be statistically significant for DSC litigations; (3) implemented 4 weighing mechanism for data representation, namely Term Frequency, Logarithmic Term Frequency, Augmented Term Frequency, and Term Frequency Inverse Document Frequency; and (4) employed Machine Learning (ML) classifiers, namely Naïve Bayes, Decision Tree, and PART for the development of 12 prediction models. Among the finding of this study (1) ML classifiers present a suitable solution for the analyzed task; and (2) Naïve Bayes classifiers achieved the highest prediction accuracy of 88%.

Abstracts:Current practice in selecting plants in the field of landscape design is based largely on aesthetic criteria, rather than cost and water consumption needs. This typically results in a design that neither minimizes the Life Cycle Cost (LCC) to the fullest nor optimizes the sustainability of water resources. This paper presents an Automated Optimizer for Sustainable Urban Landscape design (SEOUL) that supports landscape architects in overcoming the drawbacks of current practice. It consists of a database module that includes the list of plants from which the tool makes its selection, as well as an optimization model that uses dynamic programming to optimize the plant selection through minimizing initial cost and water needs. SEOUL is applied on selected case study projects in Egypt to demonstrate the functionality. It was validated by comparing SEOUL's results to those received from current practice through three actual projects. The results obtained shows a 44% and 33% savings in initial cost and water consumption respectively when using SEOUL versus traditional landscape design tools. In addition, the application's ergonomics was evaluated for ease of use, simplicity and efficiency for the end-users.

Abstracts:This paper looks at the challenge of performing Computational Fluid Dynamic simulations against models of real world environments and objects. The difficulty for these simulations is the effort required to render complex objects and environments, as well as the computational power required to solve them. To assist with the first problem, this paper presents a process for importing Building Information Models or 3D CAD models into ANSYS workbench, a popular CFD environment in industry. In order to address computational concerns, this paper also introduces and discusses a system, LODOS, designed to aid in importation and set-up of high complexity models by selectively reducing geometric complexity. This paper presents preliminary results from the use of this system to import two Building Information Models into ANSYS Workbench, showing the current strengths of the system, as well as discussing its current limitations.

Abstracts:Independent metering valve control system can overcome the shortcomings of the traditional four-side linkage valve controlled system, such as poor controllability and large energy consumption, especially under the overrunning load condition. Current researches mainly focus on the velocity, pressure and energy consumption characteristics of the hydraulic system. However, with the intelligent development of mobile machinery and the continuous improvement of work quality requirements, each actuator should not only meet the velocity and output force requirements, but also achieve high positioning accuracy. Therefore, according to the independent metering valve control system and pump-valve hybrid control principle, a velocity and position combined control strategy based on mode switching is proposed to control the boom and arm of hydraulic excavator. Then, a mechanical-hydraulic co-simulation model including the whole hydraulic excavator is established to verify the strategy, predict control characteristics and determine the controller parameters. Furthermore, a test prototype based on the above principle is established, and the boom and arm operating characteristics with the proposed strategy are tested and analyzed. The results show that the hydraulic cylinders can move smoothly along the expected trajectory under the premise of low energy consumption. The operate velocity fluctuation is small and the positioning error to the target position is about 1 mm. Due to the boom cylinder and arm cylinder in the research are separately typical two and four quadrants working actuators, the research work has universal significance to smooth, high-precision and low energy consumption automation operation of various types of mobile machineries.

Abstracts:While several concrete waviness assessment methods are being developed to overcome the disadvantages of one assessment method over the other, the sparseness of measurements associated with each method prevents from achieving a better understanding of how elevations and undulations change across the surface. Assessing waviness over multiple one-dimensional (1D)-survey lines may not accurately reflect the actual condition or waviness of the entire floor. The methodology presented in this paper presents a compliance-checking algorithm for detecting elements where dimensions exceed specified tolerance. It also enables assessment of a concrete surface in two-dimensional (2D) domain using the synergy of Terrestrial Laser Scanning (TLS) and Continuous Wavelet Transform (CWT). 2D CWT analysis provides information not only about the periods of the surface undulation, but also the location of such undulations. The validity of the methodology is established by running a test on point clouds obtained from a warehouse project near Gresham, Oregon. A rigorous comparison between one of the existing floor waviness measurement methods, the waviness index method, and the proposed method is made. The results showed that the proposed methodology delivers accurate results that enable the localization of surface undulations of various characteristic periods. Furthermore, the proposed method is more efficient in terms of time taken for acquiring the measurements, and is, thus, more cost efficient.

Abstracts:The construction sector is currently undergoing a shift from stick-built construction to modular building systems that take advantage of modern prefabrication techniques. Long established in-situ construction practices are thus being replaced by processes imported from the manufacturing sector, where component fabrication takes place within a factory environment. As a result of this transformation, current construction supply chains, which have focused on the delivery of raw materials to sites, are no longer apt and need to make way to new, strengthened, and time-critical logistics systems. The aim of this study is to establish a mathematical model for the optimisation of logistics processes in modular construction covering three tiers of operation: manufacturing, storage and assembly. Previous studies have indicated that construction site delays constitute the largest cause of schedule deviations. Using the model outlined in this paper we seek to determine how factory manufacturing and inventory management should react to variations in the demand on construction sites. A two-stage stochastic programming model is developed to capture all possible demand variations on site. The model is evaluated using a case study from the residential construction sector. The application shows that the model is effective and can serve as decision support to optimise modular construction logistics.

Abstracts:Recently, with a growing number of high-rise buildings in cities, interest in building facade maintenance is increasing. The existing method of cleaning the exterior walls of existing high-rise buildings depended on the methods by workers who used ropes, gondolas, and winch systems. Recently, however, BMU (building maintenance unit) has been developed and applied to resolve safety problems and boost work efficiency. In Germany, USA, France and other countries, various types of robot systems for building façade maintenance are being applied. In South Korea, façade cleaning robots attached with curtain walls are also being developed. In this paper, we propose an integrated control system for the stable control of robots with the building façade cleaning technology. The proposed control system can be divided into three stages such as preparation stage, cleaning stage, and return stage. Each independent robot system performs tasks such as cleaning, moving, and obstacle detection according to each stage. A wireless communication system for stable communication between robots was proposed and applied for controlling the robot system. The proposed integrated control system was applied to building façade cleaning robots and its efficiency was verified compared with existing high-rise building cleaning methods.