The estimation of construction costs for shield tunneling projects is typically based on a standard quota, which fails to consider the variation of geological parameters and often results in significant differences in unit cost. To address this issue, we propose a novel model based on a random forest machine learning procedure for analyzing the construction cost of shield tunnelling in complex geological conditions. We focus specifically on the unit consumption of grease, grouting, labor, water, and electricity. Using a dataset of geotechnical parameters and consumption quantities from a shield tunneling project, we employ KNN and correlation analysis to reduce the input dataset dimension from 17 to 6 for improved model accuracy and efficiency. Our proposed approach is applied to a shield tunneling project, with results showing that the compressive strength of geomaterial is the most influential parameter for grease, labor, water, and electricity, while it is the second most influential for grouting quantity. Based on these findings, we calculate the unit consumption and cost of the tunnelling project, which we classify into three geological categories: soil, soft rock, and hard rock. Comparing our results to the standard quota value, it is found that the unit cost of shield tunneling in soil is slightly lower (6%), while that in soft rock is very close to the standard value. However, the cost in the hard rock region is significantly greater (38%), which cannot be ignored in project budgeting. Ultimately, our results support the use of compressive strength as a classification index for shield tunneling in complex geological conditions, representing a valuable contribution to the field of tunneling cost prediction.

/pdf/intelligent-analysis-of-construction-costs-of-shield-2sevoyrr.pdf

Intelligent Analysis of Construction Costs of Shield Tunneling in Complex Geological Conditions by Machine Learning Method

Using computer vision and machine learning to identify bus safety risk factors.

Localizing safety performance functions for two-way STOP-controlled (TWST) three-leg intersections on rural two-lane two-way (TLTW) roadways in Alabama: A geospatial modeling approach with clustering analysis.

Modeling Spatiotemporal Heterogeneity in Interval-Censored Traffic Incident Time to Normal Flow: A Geographically and Temporally Weighted Proportional Hazard Analysis

Cyber-physical models for distributed CAV data intelligence in support of self-organized adaptive traffic signal coordination control

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

Spatiotemporal Kernel Density Clustering for Noisy Crowdsourcing Data for Wide Area Near Real-Time Pothole Detection

Monitoring large trucks (LT) plays an essential role in the operation and planning of transportation and economics. Although many “advanced” traffic detectors (e.g., infrared, microwave, and magnetic sensors) can identify LT, those technologies are not as widely deployed as loop detectors, and their performance is disputed. While many previous studies have investigated estimation of truck traffic using gross volume and occupancy information from loop detectors, most of them are based on mean effective vehicle length (MEVL), and thereby vulnerable to dynamic traffic conditions and dependence on exogenous data sources. This paper aims to develop an original method of estimating LT volume using detector output (i.e., volume and occupancy). A linear optimization problem is formulated by minimizing the difference between observed occupancy and theoretical “best guess.” LT and short vehicle (SV) lengths and their counts are devised as decision variables to compensate for variations in speed and length. To assess the proposed method, volume and occupancy data output in 30 s granularity from three different cases are prepared: microscopic simulation, Tennessee Radar Detector System (TN-RDS), and California Freeway Performance Measures System (PeMS). For the simulation, 12 different scenarios were built up and the results indicate that the mean absolute error of LT estimates increases with traffic volume and truck penetration. In two real-world cases, the LT estimates are close to ground truth from video and weigh-in-motion (WIM) station records. Furthermore, this simple and online LT volume estimate approach can be extended to other stations and to monitor a wide range of LT activities.

Innovative Method for Estimating Large Truck Volume Using Aggregate Volume and Occupancy Data Incorporating Empirical Knowledge Into Linear Programming

 Geo-spatial information technology can provide data resources, positioning benchmarks, basic framework and key technologies for disaster prevention and reduction At present, there are some problems in China’s disaster reduction services Too much focus is placed on decision-making but not on early warning In addition, the integration of technology, system and application of disaster reduction services is not enough There is still a lack of a unified understanding an integrated disaster reduction intelligent service system In order to provide technical support for China's comprehensive disaster reduction decision analysis, from the perspective of surveying, mapping, and geoinformation, this paper introduces an integrated disaster reduction intelligent service prototype system, which is concluded key technologies such as indoor and outdoor integrated emergency location, multi-source emergency data fusion, disaster scene visualization, and disaster model analysis services Moreover, the integrated disaster reduction intelligent service prototype system has been applied in relevant emergency departments in Tibet and Xinjiang, realizing the integrated sensing, positioning, integration, analysis and service of emergency information

/pdf/the-integrated-disaster-reduction-intelligent-service-system-4kq5iqok6z.pdf

Yang Gu

Papers

Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.

Spatiotemporal Kernel Density Clustering for Noisy Crowdsourcing Data for Wide Area Near Real-Time Pothole Detection

Innovative Method for Estimating Large Truck Volume Using Aggregate Volume and Occupancy Data Incorporating Empirical Knowledge Into Linear Programming

The Integrated Disaster Reduction Intelligent Service System and its Application