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Shake-table tests of a full-scale 3-story reinforced concrete frame building were performed at the E-Defense facility (Miki City, Japan) as part of the Tokyo Metropolitan Resilience Project. Building data were collected before and after each shake-table experiment, using light detection and ranging (lidar) scanning technology. Three-dimensional point clouds were generated and processed to investigate the damage features that can be extracted, which are important for postdisaster structural assessment purposes. To this end, this paper discusses aspects pertaining to lidar data processing and registration, calculation of residual displacements and interstory drifts, and concrete cracking and spalling identification and measurement. Most notably, this paper deals with very large data sets, processed to extract structural response information at both global and local scales. Limited multiscale work has been done in the past, and the different sets of challenges that arise have typically been addressed separately. Evidently, this gap needs to be filled before lidar can be effectively used in the field for structural assessment applications. A series of examples in which lidar data are processed and analyzed to assess specific structural components is illustrated, showing that point cloud data can indeed be processed to obtain measurements of global residual drifts or of the extent of visible damage (e.g., crack width and spalling). Although preliminary in nature, the results presented in this paper indicate that collecting postdisaster data using terrestrial lidar scanners has the potential to contribute improving how buildings are assessed in the aftermath of seismic events. [ABSTRACT FROM AUTHOR]

In the Chengdu-Chongqing economic circle in China, the recommended bearing capacity of a medium-weathering mudstone foundation is less than the capacity of the rock structure to withstand loads. The aim of this paper was to perform plate loading tests (PLTs) with binocular vision measurement (BVM) to obtain the accurate deformation of the foundation under vertical loading. The results revealed that vertical load–bearing foundations have a failure mode that can be reduced to that of thrust failures for thin bedded structures of mudstone foundations. By analyzing the differences in results between the PLTs and the uniaxial compressive strength tests under the same conditions, a new formalism for calculating foundation-bearing capacity was proposed; there is only a 5% difference compared to in situ tests. The results of this research provide theoretical support and a practical benchmark for the full development of the load-bearing capability of soft red rocks. As the Chengdu-Chongqing economic circle progresses toward the international urban cluster, inevitably, soft rock is used as a foundation retaining layer in the construction of superhigh and super-rise buildings in the region. However, owing to antiquated testing methods and poor intelligibility of the testing equipment, the bearing capacity of such a mass of rock has yet to be fully developed and cannot meet the dual-carbon economic goals and sustainable development of architectural design. Based on this, the study introduces a noncontact method to measure the bearing capacity of soft rock within the traditional method of in situ testing and combines the concept of big data analysis to advance an empirical method for calculating the bearing capacity accurately. Based on the results of this study, the concept of natural foundations can be adopted and the engineering dilemma that the treatment of building foundations cannot be economical and practical can be solved. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting could provide a scientific basis for the rapid response and stable operation of a modern power system. To take advantage of the meteorological big data to improve short-term forecasting accuracy, while considering the nonlinear and spatiotemporal correlation characteristics of the power load data, this paper proposes a short-term power load forecasting method based on meteorological data dimensionality reduction and a hybrid deep neural network. First, the elastic network is used to reduce the dimensions of high-dimensional meteorological big data, eliminate irrelevant meteorological factors, and improve the quality of input data. Then, taking the dimension-reduced meteorological data and historical load data as input, a load forecasting model based on a novel deep neural network is established. This model uses a convolution neural network (CNN) and a bi-directional long short-term memory (BiLSTM) neural network to extract the spatial and temporal correlation features of power load related data, and combines the attention mechanism to enhance the learning weight of the load series in important periods, and adopts residual connection (RC) to optimize the network training speed and alleviate the overfitting problem. Finally, taking the open data set of the New York Independent System Operating Agency (NYISO) as an example, single-step and multi-step advance prediction experiments are carried out to verify the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

During subway tunnel construction, the consumed time for each ring (unit of construction progress) is highly dependent on objective factors such as geological conditions and shield performances, as well as subjective reasons including workers' proficiency and contractors' management skills. It is nontrivial to score each ring's efficiency from an objective angle, so that workers and contractors can receive fair evaluations. In this paper, a scoring method driven by construction big data is proposed. First, to resolve the high-dimensionality problem, a Gaussian mixture model (GMM) was employed to cluster rings of similar conditions in a probabilistic style so that detailed information can be retained. Second, the standard time to finish one ring was analyzed, so that each ring can be labeled as fail or pass, and our task can be considered as a classification problem. Third, for each cluster, a broad learning system (BLS) was developed as a classifier due to its advantages of fast computation and incremental learning. Finally, the BLS was trained with real tunneling data of 23,822 rings, and then scorecards were developed, where results of validation and statistical tests suggested that our method outperforms conventional ones. Feedback from the subway company and two compared contractors suggested that the proposed method is fair and practical, and it could reveal management problems that were easily overlooked. For subway shield tunnels, this study proposes a construction progress scoring method driven by big data for subway companies to evaluate the performance of each construction unit, e.g., each ring of the shield tunnel is treated as a sample analysis. First, in view of the difficulty of many geological types and risk variables, the Gaussian mixture method is used to classify the samples and obtain probability that each ring belongs to a certain geological type. Statistical analysis is used to calculate the shield standard time of each geological type, so that the time interval of pass can be determined. Second, a broad learning model with low calculation and updating cost is constructed to fit the mapping relationship between geological factors and construction progress. With this model, each ring can be scored. Finally, through the model training and verification of 23,822 ring data in seven subway intervals, results suggest that the method is practical. Our method can objectively reflect the work efficiency and management ability of different contractors and workers. In addition, suggestions can be elicited to improve the management level of construction units. [ABSTRACT FROM AUTHOR]

Dense three-dimensional (3D) point clouds collected from rapidly evolving data acquisition techniques such as light detection and ranging (lidar) and structure from motion (SfM) multiview stereo (MVS) photogrammetry contain detailed geometric information of a scene suitable for a wide variety of applications. Among the many processes within a typical point cloud processing workflow, segmentation is often a crucial step to group points with similar attributes to support more advanced modeling and analysis. Segmenting large point cloud data sets (i.e., hundreds of millions to billions of points) can be extremely time consuming and tedious with current tools, which primarily rely on significant manual effort. While many automated methods have been proposed, the practicality, scalability, and versatility of these approaches remain a bottleneck stifling processing of large data sets. To overcome these challenges, this paper introduces a novel, generalized segmentation framework called Vo-Norvana, which incorporates a new voxelization technique, a normal variation analysis considering the positioning uncertainty of the point cloud, and a custom region growing process for clustering. The proposed framework was tested with several large-volume data sets collected in diverse scene types using several data acquisition platforms including terrestrial lidar, mobile lidar, airborne lidar, and drone-based SfM-MVS photogrammetry. In evaluating the accuracy of models generated from Vo-Norvana against manual segmentation, the average error of the position, orientation, and dimensions are 2.7 mm, 0.083°, and 0.9 mm, respectively. Over 0.2 million points per second and 36 thousand voxels per second can be achieved when segmenting an airborne lidar data set containing over 639 million points to about 1 million segments. [ABSTRACT FROM AUTHOR]

This paper presents two new solutions for transition curves that make it possible to create a smooth connection between two arbitrarily located points. For this reason, the curves are referred to as universal transition curves. The solutions are based on Degree 4 and 5 polynomials and describe the entire curved transition between the set points using only a single equation. The paper also describes a procedure for designing an arc with the use of these curves. The design process requires exact information concerning the location of the two points to be joined (i.e., P and K) as well as the directions of the tangents at these points together with the curvature radii at P and K. Such a data set is necessary owing to the form of the equations describing the universal transition curves. The design principles have been illustrated with relevant numerical examples. [ABSTRACT FROM AUTHOR]

Historically, structural health monitoring (SHM) has relied on fixed sensors, which remain at specific locations in a structural system throughout data collection. This paper introduces state-space approaches for processing data from sensor networks with time-variant configurations, for which a novel truncated physical model (TPM) is proposed. The state-space model is a popular representation of the second-order equation of motion for a multidegree of freedom (MDOF) system in first-order matrix form based on field measurements and system states. In this mathematical model, a spatially dense observation space on the physical structure dictates an equivalently large modeling space, i.e., more total sensing nodes require a more complex dynamic model. Furthermore, such sensing nodes are expected to coincide with state variable DOF. Thus, the model complexity of the underlying dynamic linear model depends on the spatial resolution of the sensors during data acquisition. As sensor network technologies evolve and with increased use of innovative sensing techniques in practice, it is desirable to decouple the size of the dynamic system model from the spatial grid applied through measurement. This paper defines a new data class called dynamic sensor network (DSN) data, for efficiently storing sensor measurements from a very dense spatial grid (very many sensing nodes). Three exact mathematical models are developed to relate observed DSN data to the underlying structural system. Candidate models are compared from a computational perspective and a truncated physical model (TPM) is presented as an efficient technique to process DSN data while reducing the size of the state variable. The role of basis functions in the approximation of mode shape regression is also established. Two examples are provided to demonstrate new applications of DSN that would otherwise be computationally prohibitive: high-resolution mobile sensing and BIGDATA processing. [ABSTRACT FROM AUTHOR]

Recent research undertaken revealed that a significant positive relationship exists between quality and safety performance. A major limitation of this research, however, was the nature of the sample; it was heterogeneous (i.e., a combination of U.S. and international projects) and the sample was restricted to 18 projects. Building upon initial research, this paper re-examines the association between quality and safety using a homogeneous sample of 569 projects, which were derived from an Australian construction company with an annual turnover in excess of $1 billion Australian dollars (AU$). A total of 19,314 nonconformances and 17,783 injuries were used to determine the validity and reliability of previous research. A weak association between quality and safety performance was found (p < 0.01). The p-values did not indicate any significant association between first aid and quality rates, except for the injury rate and rework frequency per million scope, which yielded an r-value of 0.307 and p-value 0.046 that is significant at 0.01 level. An association, however, between injuries and rework was identified (r² = 0.70). The discrepancy between this research's findings and that of previous work led to an examination of the issues of using ratios in correlation analysis. Thus, the statistical and arithmetic issues associated with the use of ratios are discussed, and it is recommended that estimating the relationships between quality and safety should be examined using regression techniques or analysis of covariance. Linear regression, therefore, was performed with the injury data as the dependent variable, and rework frequency and personnel hours as the independent variables. The regression results demonstrated that there is a significant association between injuries, and rework and personnel hours; it was revealed that both predictors accounted for 68.2% of the explained variability in injury frequency. The replication of the initial research has enabled a significant advancement in knowledge about relationship between quality and safety performance. [ABSTRACT FROM AUTHOR]

This paper presents a closed-form procedure for the coarse registration of three-dimensional (3D) point clouds using automatically extracted linear features, which have beenmanually matched. Corresponding linear features are defined by nonconjugate endpoints that do not necessarily define compatible direction vectors. Because the point clouds could be derived from different sources (e.g., laser scanning data sets and/or photogrammetric point clouds that are referenced to arbitrary reference frames), the proposed procedure estimates the scale, shift, and rotation parameters that relate the reference frames of these data sets. The proposed approach starts with a quaternion-based procedure for initial estimation of the transformation parameters using the minimal number of required conjugate line pairs (i.e., two noncoplanar linear features from each point cloud). The initial estimate of the transformation parameters is then used to ensure the compatibility of the direction vectors of the involved linear features. The modified direction vectors together with the endpoints of the linear features are used for deriving a better estimate of the transformation parameters. Experimental results from both simulated and real data sets verified the feasibility of the proposed procedure in providing good quality for the approximate parameters of the transformation parameters for point-based fine registration procedures. [ABSTRACT FROM AUTHOR]

This paper proposes a big-data approach to automatically identify and extract buildings from a digital surface model created from aerial laser scanning data. The approach consists of two steps. The first step is a MapReduce process where neighboring points in a digital surface model are mapped into cubes. The second step uses a non-MapReduce algorithm first to remove trees and other obstructions and then to extract adjacent cubes. According to this approach, all adjacent cubes belong to the same object and an object is a set of adjacent cubes that belong to one or more adjacent buildings. Finally, an evaluation is presented for a section of Dublin, Ireland, to demonstrate the applicability of the approach, resulting in a 91% quality level for the extraction of 106 buildings over 1 km², including buildings that have more than 10 adjacent components of different heights and complicated roof geometries. The proposed approach is notable not only for its big-data context but for its usage of vector data. [ABSTRACT FROM AUTHOR]

As society and technology change the way public transit systems are perceived, contemporary professionals need to be equipped with the skills to manage transportation systems to keep up with modern demands and challenges. Information Communication Technologies and data ubiquity represent great potential advances in transport systems; however, the challenge of transferring the knowledge from research and academia remains a significant barrier. This paper contributes to the literature in two ways: it serves as a benchmark for the challenges in transit systems planning education today, and it is a reference guide for future educators to find resources to create and refine effective educational programs in this area. A case study of a sample teaching module for transit systems planning in the Greater Toronto Area is presented with guidelines to teach (1) models of data-driven flexible transit services; (2) technologies to integrate and visualize user and systems data; and (3) methodologies to evaluate demand for such services at a societal level. [ABSTRACT FROM AUTHOR]