Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.

In this paper, we propose to conduct a dynamic monitoring and vibration analysis of ancient bridges by means of ground-based microwave interferometry and the extreme-point symmetric mode decomposition (ESMD) method. Ground-based microwave interferometry, a novel non-contact technology with a high accuracy, is used to acquire dynamic time series displacements with environmental excitation factors and a transient load with a car, respectively. The ESMD method, a new alternative to the Hilbert-Huang transform (HHT), is adopted to conduct the instantaneous vibration analysis of Zhaozhou Bridge. Firstly, a series of intrinsic mode functions (IMFs) are obtained together with an optimal adaptive global mean (AGM) curve by using a mode symmetric about the maxima and minima points. Secondly, the instantaneous frequency of each IMF is obtained by the use of a direct interpolation algorithm, which can reconcile the conflict between the period and the frequency for the traditional time-frequency analysis methods. As a representative case, Zhaozhou Bridge, a well-known Chinese ancient bridge constructed more than 1400 years ago, is studied in detail. Four kinds of dynamic time series displacements—two of them acquired by considering only environmental excitation factors for the mid-span and 1/4-span points and the others obtained with the transient load of a car for the mid-span and 1/4-span points—are selected to pursue a comparison of the decomposed IMFs and the instantaneous frequencies to perform the instantaneous vibration analysis of Zhaozhou Bridge. By comparing the results obtained with HHT for the decomposed IMFs and the instantaneous frequencies, the results show that the proposed method has a powerful ability to evaluate the instantaneous dynamic response of ancient bridges.

In response to the issues of low outdoor thermal comfort and poor ventilation environment in Beijing Hutong, this paper proposes a rapid intelligent optimization method combining Pix2Pix (Image-to-Image Translation with Conditional Adversarial Networks) with a genetic algorithm. Firstly, the architectural types of the research objects are highly refined and summarized into four traditional building types. Then, they are placed in the site with open spaces in a certain proportion, and a multi-objective optimization model for the UTCI (Universal Thermal Climate Index) and building area is constructed using a genetic algorithm, generating and iteratively optimizing the spatial layout of the building population. Finally, Pix2Pix is used to learn and train a large number of Hutong combination samples, rapidly generating the UTCI and ventilation results, which serve as the optimization objectives to obtain the optimal solution set for Hutong spatial forms. Compared with traditional empirical design methods, this method allows for a rapid and efficient traversal of vast solution spaces, intelligently generating Hutong renovation schemes that balance cultural heritage and healthy comfort. The research results demonstrate that this method can quickly find (26.4 times faster than traditional performance simulation methods) that the reasonable proportions of Siheyuan, Sanheyuan, Erheyuan, new buildings, and empty spaces in the Da Yuan Hutong in Beijing should be controlled at 11.8%, 16.9%, 23.8%, 33.8%, and 13.7%, respectively. Meanwhile, the building density should be maintained between 0.5 and 0.58, and the floor area ratio should be kept between 0.96 and 1.14. This significantly improves outdoor comfort, enhances the living environment of the Hutong, and promotes sustainable urban development.

With the advantage of non-contact measurement, ground-based synthetic aperture radar (GB-SAR) has been widely used to obtain the dynamic deflection of various bridges. Data-driven stochastic subspace recognition (Data-SSI), a popularized time-domain technique, is commonly used for modal parameter identification of bridges. To improve the computational efficiency and accuracy of the Data-SSI method for bridge modal parameter estimation using GB-SAR, this paper proposes an improved Data-SSI method. First, boxplot data filtering is applied to screen out the error points to generate a Hankel matrix. Second, the Hankel matrix compression method is presented to reduce the ill-conditioned vectors in the column vectors of the Hankel matrix to improve calculation efficiency. Finally, the exact modal order (EMO) modal estimation algorithm based on the autocorrelation matrix is adopted to reduce the generation of false modes and improve the calculation efficiency. The results of simulation and field experiments show that the natural frequency values for the improved Data-SSI method are 2.3208 and 2.3189 and the damping ratio coefficient values are 8.10 and 8.08, under windows 1 and 2, respectively. The operation times using the improved Data-SSI method are 2.02 s and 7.61 s under windows 1 and 2, respectively. This proves that the proposed improved Data-SSI method has higher accuracy and computational efficiency.

Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR—especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles—an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.

The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision. In this study, we utilize the characteristics of 3D terrestrial lidar point cloud data to build a CSG-BRep 3D model of underground cable wells, whose spatial topological relationship is fully considered. In order to simplify the modeling process, first, point cloud simplification is performed; then, the point cloud main axis is extracted by OBB bounding box, and lastly the point cloud orientation correction is realized by quaternion rotation. Furthermore, employing the adaptive method, the top point cloud is extracted, and it is projected for boundary extraction. Thereupon, utilizing the boundary information, we design the 3D cable well model. Finally, the cable well component model is generated by scanning the original point cloud. The experiments demonstrate that, along with the algorithm being fast, the proposed model is effective at displaying the 3D information of the actual cable wells and meets the current production demands.