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The most promising way of verifying these experimental research data is computer simulation of structures, also during a fire.
Despite these advancements, there remains a gap in integrating physical damage data with predictive modeling for war-damaged buildings.
The proposed methodology integrates numerical modeling, field observation data, and structural analysis tools to provide a practical framework for post-blast assessment and reconstruction planning.
Methods of Inspection: – Familiarization with the object, available project and technical documentation; – Analysis of initial data; – Conducting visual and instrumental inspections of the building's structural elements, engineering systems, and the object as a whole; – Clarification of operating conditions; – Identification and documentation of existing damage based on the conducted inspection; – Analysis of inspection results and preparation of conclusions with classification of the building according to the specified groups, as well as providing final recommendations for priority and emergency measures.
Experimental and instrumental surveys of the damaged building confirmed the reduction in concrete strength, the disruption of the structure of frame elements, and the need for major repairs or strengthening of several structural components.

The system creates the rational signal timing scheme and optimize the scheme according to different periods through the data collecting module or user’s input.
The system mainly include three parts: the first is the data input platform, which has the function of inputting the basic data needed in the signal timing process; the second part is platform for realizing signal timing, which is mainly based on the improved TRRL model mentioned in section 2;the third is evaluation platform, which can realize effect evaluation for the signal timing scheme.
The design for database code, data structure and data maintaining system are very crucial to the system since the software involves the large scale basic data.
The database in the software mainly includes geometric feature data and traffic flow data.
The traffic flow data mainly involves the flow of motors, non-motors and pedestrians and so on, as shown in Table.1.

Its main function is to collect the information of environment parameters and the data through the wireless communication module convergence to the coordinator gateway.
Fig.3 Sensor node hardware architecture The design of intelligent irrigation decision system The architecture design for data transmission According to data acquisition, transmission, storage, release of information flow and the system network architecture, this paper designs the system software architecture that is shown in figure 4.
From the node's data is stored in a database, the database monitoring Web server, and trigger the corresponding instruction, sending and receiving process control node.
Conclusions Experiment shows the system prototype implemented in this paper has the following functions: (1) it can real-time acquire field information, and data can be stored in the agricultural environmental monitoring center server; (2) the remote user can access server and the coordinator gateway through the Internet Webpage browser, and to control the whole system and data download; (3) it can communicate between the wireless sensor network , the Internet and 3G mobile communication network with high-speed access.
Status quo and progress of data transmission and communication technology in field information acquisition[J].

Statistical analysis: multivariate analysis Data (dependent and independent factors) were analysed using a combination of constrained and unconstrained multivariate statistical methods in order to account for both total variation in the data and variation explainable by environmental data.
Canonical Correspondence Analysis (CCA) was used to compare the microbial community data with environmental data.
Tests were performed by using the multivariate data analysis software CANOCO 4.5 (Microcomputer Power, Ithaca, NY, USA) [11].
This was confirmed by the Chao1 estimator (data not shown).
Canonical Correspondence Analysis (CCA) was used to correlate microbial community data with environmental data.

When the companies face different customers, the design system can be corresponded quickly, the results can fit the existing manufacturing system of the enterprise. 1.2.2 The ability to use the patent of technological innovation[1] Patent indicators are the most intuitive data when measuring the product development in technological innovation of a company
(3)Data processing.
Quantitative data and qualitative indicators source data are from the company's annual report, field survey and expert scoring.
The origin data of evaluation is shown in Table 3.
The data were using standardized evaluation data on Table 4.

The data shown reveals the potential of high-ĸ dielectric based sensors, to operate in extreme and isolated conditions for extensive amounts of time without the need for maintenance.
The data shows that the most significant change in COX occurs in the first hour of exposure, when it reduces by approximately 8%, from 64pF to 59pF.
The data reveals that during the first hour of exposure, VFB increases significantly, from 1.3V to 3V.
As exposure time increases, the data in Fig. 4 reveals that the density of interface traps (DIT) increase at the energy levels considered.
The data presented in Fig. 2, 3 and 4, demonstrates that the Pt/HfO2/SiO2/SiC structure can function reliably at 400°C for a period of 1000 hours.

MBD (Model Based Definition) uses data set instead of engineering drawings which brings revolutionary change in the means of production.
The integrated digital model, also known as data set, mainly includes the geometry information and the non-geometry information.
Using the 3D model as the unique source of definition, we can eliminate the ambiguity between the 3D model, paper drawings, and downstream applications requiring data, which will greatly improve the quality and efficiency of engineering.
The crux of the idea was to extend the usage of digital models in the reduction or elimination of 2D drawings[4].
The ASME Y14.41 standard includes several parts: general, data set identification and control, data set requirements, design model requirements, common requirements for product definition data, notes and special notations, model values and dimensions, plus and minus tolerances, datum applications and geometric tolerances[5].

Growth and substrate depletion data may fit to a simple Monod model as stated in Equation 1.[1], [2].
The experimental data consisted of initial substrate concentration over a given sampling period.
The fourth order Runge – Kutta method was used for fitting the Monod equation to the experimental data.
The model was calibrated using five sets of data and example of data as shown in Table 2.
The lowest average of error squares show the best data set that predic the fitting values.

Because INSAR can image in day and night and under all weather, researching on INSAR data processing has became very important in this area.
Meanwhile, the accuracy of this step is crucial to the reliability of subsequent image processing and final results of the data processing chain, meeting sub-pixel precision request.
Flat earth removal is an important step of INSAR data processing
So the technique of flat earth removal is an important step in data processing of INSAR and its accuracy effects on the precision of the DEM.
Interferometric SAR Phase Noise Reduction [J].

This structured DFT methodology could get an optimal compromise between test coverage and test cost such as chip area, pin numbers, test vectors data size, test time and so on.
DFT design flow When running the memory testing, the data, addresses and control signals applied to RAM are generated by the BIST and then the BIST comparator checks data read from RAM and generates pass/fail signals.
In order to increasing the fault detection, we use two data backgrounds on this algorithm, all 0s and 01s interleave.
These scan chains are too long, which means the volume of scan test data will exceed the ATE’s data volume and the test time will be too long to be accepted.
By using EDT, the test data volume and the test time of this chip are dramatically reduced, without decreasing the test coverage.