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This paper provides a comprehensive review of tree-based models and their application in condition assessment and prediction of water, wastewater, and sewer pipe failures. Tree-based models have gained significant attention in recent years due to their effectiveness in capturing complex relationships between parameters of systems and their ability in handling large data sets. This study explores a range of tree-based models, including decision trees and ensemble trees utilizing bagging, boosting, and stacking strategies. The paper thoroughly examines the strengths and limitations of these models, specifically in the context of assessing the pipes' condition and predicting their failures. In most cases, tree-based algorithms outperformed other prevalent models. Random forest was found to be the most frequently used approach in this field. Moreover, the models successfully predicted the failures when augmented with a richer failure data set. Finally, it was identified that existing evaluation metrics might not be necessarily suitable for assessing the prediction models in the water and sewer networks. [ABSTRACT FROM AUTHOR]

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]

Urban national parks (UNPs) provide tourism services in cities worldwide. However, the factors affecting tourist distributions in UNP activity and path spaces remain uncertain. Using Web crawler technology, location big data were tracked and sampled in Donghu National Park in Wuhan, China, and 12 predictor variables were analyzed using a machine-learning method (i.e., random forest). The consistency of the big data compared to the population census and tourist observations was determined at 79.5% and 77.8%, respectively. The tourist number (p) and tourist density (p/ha) per day in the park space in Donghu National Park were 0–2,531 p and 0–198.0 p/ha, respectively. Peak tourist periods showed pressure flows of 0.3–34.5‰ between scenic areas in the park. An analytical framework was formulated for UNPs to link the urban environment, park attributes, and configurational attributes, which here explained 66.4%–72.5% of the tourist distribution in the path and activity spaces. Random forest models performed better than geographically weighted regression (GWR) or ordinary least squares (OLS) models, indicating a complex nonlinear relationship between the independent variables and tourist distribution in UNP spaces, rather than the linear relationship that has previously been found in urban parks. First, both activity and path spaces near developed urban environments or park entrances bore higher tourism pressure. Second, winding routes attracted tourists to path spaces, while water landscapes attracted tourists to both path and activity spaces. Third, tourism pressure in path spaces was determined by configurational attributes. These results are important reference points for the planning and management of UNPs. [ABSTRACT FROM AUTHOR]

The development of urban underground space (UUS) is an important approach to the sustainable development of modern cities. This means that assessing potential demand differences in undeveloped UUS can inform the areas that need to be prioritized for development in urban planning and development. The recent emergence of geospatial big data provides fine-grained support for capturing changes in the development of various urban subjects. However, to date, research applying geospatial big data to UUS assessment has been limited. In this study, UUS evaluation indicators were constructed using geospatial Big Data, and the weights of the indicators driving the existing UUS demand were determined based on the Geodetector model, avoiding the drawbacks of human subjective influence and the lack of reference of general methods. It also combined the linear weighting method to quantify the UUS demand in undeveloped areas within the grid based on the attribute values and weights of each indicator and used the magnitude of the resulting demand values to provide a grid-scale judgment of priority areas for future underground space development. Taking the Qiantang District of Hangzhou, China, as an example, it was verified that the high-demand areas are consistent with the existing UUS distribution, indicating that the UUS demand evaluation model based on geospatial big data established in this study is feasible and accurate, and in this way, new high-demand areas were circled in the undeveloped areas as a direction for future urban underground development. [ABSTRACT FROM AUTHOR]

As an important part of the energy transportation system, oil and gas pipelines are developing toward intelligence and digitalization. Vigorously developing and constructing pipeline networks based on intelligent methods such as big data and neural networks will help improve the efficiency of operation and management. This work provides an overview of recent developments of intelligent methods, including machine learning approaches, heuristic algorithms, and mathematical programming, used in the pipeline industry that can provide beneficial support for various applications enhancing operation and maintenance. Several aspects such as operating condition recognition, safety monitoring, pipeline remaining life prediction, and fault detection are reviewed and discussed. The study shows the need to focus on improving management level and ensuring pipeline safety. [ABSTRACT FROM AUTHOR]

In the era of big data, the extraction of valuable knowledge and insights becomes feasible through efficient data processing methods. In this research, we establish an analytical framework enabled by big data to aid in the management of construction workers' unsafe behaviors. To evaluate workers' behavioral patterns, a multistage data processing model is employed, uncovering previously unknown connections between unsafe act records and situational data. Leveraging the high-level knowledge derived from this analysis, personalized safety management strategies are formulated based on individual workers' behavioral patterns. The effectiveness of the proposed framework is evaluated by comparing it to conventional management strategies across three construction sites. Results demonstrate that behavioral patterns discovered by the big data framework provide an important decision basis and achieve smart construction unsafe behavior management. [ABSTRACT FROM AUTHOR]