Seismic design of reinforced concrete frame structures pdf download

Abstract

In the era of big data, the efficient use of idle data in reinforced concrete structures has become a key issue in optimizing seismic performance evaluation methods for building structures. In this paper, based on the evaluation method of structural displacement seismic performance and based on the characteristics of high scalability and high fault tolerance of the cloud platform, the open source distributed and storage features of the Hadoop architecture cloud platform are introduced as a subproject of Apache Nutch project, Hadoop cloud platform. With features such as high scalability, high fault tolerance, and flexible deployment, the storage platform is secure, stable, and reliable. From the evaluation of the seismic performance of newly-built buildings and existing damaged buildings, according to the structural strength-ductility theory of the structure, the building structure resists earthquakes with its strength and ductility and buildings are divided into four categories. Due to the influence of time or seismic damage on the structure of reinforced concrete frame structures, their material properties are often deteriorating. Using the distributed computing design concept to efficiently process big data, a dynamic evaluation model for the seismic performance of reinforced concrete frame structures is established. A project of a story reinforced concrete frame structure was selected for calculation and analysis; the engineering example was used to verify the accuracy and efficiency of the model, and the seismic performance of the floor was analyzed. It can be seen that the initial stiffness index of the structure is not sensitive to the damage location of the structure. The platform based on the concept of distributed computing big data processing can effectively improve the efficiency and accuracy of the evaluation of reinforced concrete frame structures.

1. Introduction

The seismic evaluation theory for building structures with reinforced concrete structure performance is based on the investigation and statistics of a large number of seismic damage data and scientific analysis. With the advent of the era of big data, the accuracy, comprehensiveness, and efficiency of the evaluation of seismic performance have also increased. He et al. estimated the seismic performance of rural residential buildings in Guangzhou from the aspects of building structure, building age, and building fortification and put forward the countermeasures and suggestions for earthquake prevention and disaster reduction of rural residential buildings. The flaws of the seismic evaluation theory and the seismic design theory gradually exposed. The American scholar J. P. Moehle [1] first proposed the concept of structural seismic design thought based on structure displacement but did not use abundant building big data. This article introduces the open source distributed and storage features of the Hadoop architecture cloud platform as a subproject of the Apache Nutch project. The Hadoop cloud platform features high scalability, high fault tolerance, and flexible deployment. The storage platform is secure, stable, and reliable. MapReduce distributed computing is one of the core components of the Hadoop platform. It was first introduced by Google. It is a model and framework for computing task processing in the context of big data [2]. MapReducer’s design ideas can be divided into three levels: (1)First of all, the parallel computing concept is adopted to divide large-scale and massive data into multiple small data blocks, which is independent of other data blocks, and uses the same calculation method to perform parallel processing on the resources, and finally, the combined results are processed(2)The unified system architecture is used to hide the implementation details of the system, so that programmers do not have to care about distributed details to use the platform for algorithm processing and improve the development efficiency. MapReduce can autonomously complete the following aspects of the system’s underlying aspects of processing: automatic data segmentation and storage, automatically divide computing tasks and computing resources, synchronization of the node’s computing tasks and data resources, merge, load balance optimization of the overall system performance, detect all nodes in the system, and handle the failure(3)The model abstraction of distributed computing is mainly divided into map and reduce. MapReduce draws on the ideas in the Lisp functional language; Lisp defines the overall operation of the list elements and provides similar map and reduce operations. The map and reduce functions provide an abstract parallel programming interface for the system, which can quickly parallelize the data [3]. MapReduce abstracts computational processing into two operations map and reduce. The map operation is responsible for recording and repeating calculations on a group of data. The reduce operation is responsible for further integrated calculation and output of the calculation results of the map operation. It provides an open source computing platform for the data of reinforced concrete structures in building big data and realizes the efficient evaluation of reinforced concrete structures in building

2. Hadoop Dynamic Evaluation System for Seismic Performance

Hadoop Architecture

In general, the typical Hadoop architecture cloud platform is assumed to operate in a trusted environment. The typical Hadoop architecture cloud platform includes Hadoop cluster core nodes and auxiliary terminal interfaces. The cluster core nodes include: NameNode, backup NameNode, DataNode, and JobTracker members [4]. Therefore, the architecture of the Hadoop-based cloud platform is divided into three layers: the data storage layer, the middleware control layer, and the application node layer, as shown in Figure 1.

Among them, DataNode is the storage resource and computing resource of the Hadoop architecture cloud platform. Its main role is to store distributed system data and perform map-reduce tasks. Hadoop architecture cloud platform data information is stored on the DataNode terminal device. When the cloud platform receives a MapReduce job task, the NameNode node allocates the storage or computational resources of the current DataNode node through the resource scheduling policy, depending on the current task completion. For Hadoop-based cloud platforms with distributed file systems, the JobTracker plays a central role in controlling the NameNode node. It is responsible for submitting user tasks to the service running on each DataNode node of the TaskTracker, so this node also plays the role of resource application (please see Table 1).

HDFS Distributed File System

HDFS is fundamental to the unified management of stored data in distributed computing. Its features such as good fault tolerance, high reliability, high availability, high throughput, and strong expansion capability provide reliable storage protection for extracting information from a vast array of data. It has brought a lot of convenience to the research and excavation processing of massive data collection (please see Figure 2). HDFS is mainly designed to solve the following problems: (1)Inevitable System Errors. People equip the Hadoop distributed file system with a file system that can be executed on general hardware conditions. Therefore, in the course of daily use, mistakes are commonplace. Hadoop distributed file system is usually composed of hundreds of server nodes. Each node stores part of the information in all databases, and any part of this system has the probability that errors will occur at any time. Therefore, fault monitoring and rapid active restart are the main building concepts of the Hadoop distributed file system(2)Streaming Data Access. Programs executed on Hadoop distributed file systems are usually not acquired on a one-time basis but are often obtained gradually, in batches. Therefore, HDFS must support high throughput of data to better serve streaming data access(3)Mass Data Mining. With the rapid increase in the amount of data, the data to be processed is usually at or above the GB level. Therefore, a distributed file system needs to be able to provide a function of saving large files and can give a substantially higher amount of data for data transfer, which can be increased to hundreds of nodes in one cluster. Any distributed file system should be able to carry large-scale file resources. Because the process of excavating large amounts of data determines when the amount of data is very large, moving the execution process to copy it is much cheaper than migrating the data that will be analyzed. When the amount of information is very large, the effect becomes more pronounced. The overhead of migrating data is greater than the cost of the mobile computing process. This can reduce the possibility of network congestion and increase the throughput of the entire platform. In other words, it is a relatively good choice to move the calculation to the vicinity of the data, which is better than moving the data to the program execution place. Hadoop distributed file system gives the corresponding interface, so that the application can be actively moved to the data storage location to run

MapReduce Distributed Computing Model

MapReduce distributed computing is one of the core components of the Hadoop platform. It was first introduced by Google. It is a model and a framework for computing task processing in the context of big data [5]. MapReduce has changed the organization of large-scale computing and realized large-scale model abstraction of computing at the level of large-scale server clusters.

Similar to HDFS, MapReducer’s architecture is also the most commonly used master-slave structure. This master-slave architecture makes the system more scalable and robust. Introduce MapReducer’s architecture from four aspects: Job Tracker, Task Tracker, Task, and Client: (1)Job Tracker. The main track of the progress of all jobs in the system, the use of resources in the MapReduce, and the operating information submitted to the Task Scheduler. Tasks submitted to the system will be assigned to the Task Tracker to execute the corresponding job and resources and to monitor and maintain the job’s operation status. The failed job will be assigned to other Task Tracker for execution at any time(2)Task Tracker. Task Tracker is mainly responsible for job tasks allocated by Job Tracker; through the monitoring of local node resources, resources are allocated to different tasks according to usage. At the same time, Task Tracker communicates with the Job Tracker through Heart Beat timing, returns information to the Job Tracker, and accepts Job Tracker’s task scheduling(3)Task. Task is a specific calculation task, including Map Task and Reduce Task. The Map Task mainly refers to the processing in the map stage. The input file is parsed into <key, value> key-value pairs, and the key-value pairs are sorted and merged. The result is stored to the local disk and is partitioned by the partition function. The corresponding Reduce Task refers to the processing operation in the reduce stage, sorting the intermediate results after map processing and processing the Reduce function to generate a new <key, value> key-value pair and storing it in HDFS(4)Client. The client is used to interactively communicate with the MapReduce computing model. The user submits the task request through the client. The client submits the requested job to the Job Tracker. At the same time, the user can also view the running status of the task through the client

3. Evaluation Rules for Seismic Performance of New-Build Reinforced Concrete Frame Structures

Seismic Capacity Division of Building Structure

The Meaning of Earthquake Resistance

According to the theory of structural strength and ductility, the building structure resists earthquakes with its strength and ductility. When the earthquake occurs, the building structure is first resisted by the strength, the acceleration of the ground surface increases to make it yields, and then the ductility is used to resist the acceleration of the ground motion [6]. When the ductility is exhausted, the building structure will be destroyed. At this time, the acceleration of ground motion that causes structural damage is defined as the seismic capacity of the building. Therefore, in the evaluation of the seismic capability of reinforced concrete frame structures, the strength and ductility of the reinforced concrete frame structure are calculated according to the size of the actual structure of the building structure and the reinforcement, combined with the internal forces of the rod components obtained by the elastic seismic analysis of the frame. Calculate the seismic capacity of the upper and lower layers of each floor of a building structure, that is, the acceleration of ground motion sustained during the destruction .

Evaluation of Earthquake Resistance

In China’s newly published “General Principles of Design for Seismic Performance of Construction Projects (Applicable)” (CECS) according to the function of the building, the buildings are divided into the following four categories [7]: (1)IV class: use buildings that cannot interrupt or store large quantities of dangerous or toxic substances during or after an earthquake. Once the earthquake has caused the release and alienation of these items (such as toxic gases, explosives, and radioactive materials), it will cause unacceptable harm to the public(2)III class: postearthquake use of buildings or densely populated construction sites where functions must be restored in the short term or are critical for postearthquake operations, such as hospitals, schools, fire stations, police stations, communication centers, emergency control centers, and disaster relief centers(3)II class: all buildings and facilities except types IV, III, and I belong to this category(4)I class: when destroying construction earthquakes that do not endanger human life and do not cause serious property damage, such as general warehouses

Structural Failure Mode and Ductility Ratio Analysis

According to the material property and section size of the element, the strength of the structural element can be calculated according to the design code of concrete structure. However, the test results show that the bearing capacity of the element under repeated loading is lower than that under monotonic loading. Therefore, the bearing capacity of the beam and column elements calculated in this section should be multiplied by the corresponding reduction factor, where the reduction factor is [8–10].

Moment Strength at the Yield of Beams and Columns

The column section bears axial force and bending moment at the same time. In the known conditions of column section size and reinforcement, the axial force-bending relation diagram (curve) is known, as shown in Figure 3. When the point corresponding to the internal force () of the column unit falls within the curve, the unit will not be destroyed. When the column element’s internal force () is at any point on the curve, the element breaks. However, under the specific earthquake action, the failure of the unit should correspond to a certain point on a curve.

It can be seen from Figure 3 that under the action of the gravity load representative values, the generated axial force and bending moment are represented by and , respectively. Under small earthquakes (corresponding earthquake acceleration is ), the axial force and bending moment generated in the column section are expressed by and , respectively. It is assumed that when the earthquake is getting larger, the total axial force and total bending moment increase slowly along the straight line AB. When the ground motion acceleration reaches, the axial force and bending moment just intersect with the axial force and one bending moment (-) intensity map at point C, assuming that point C is the point where the unit failure is affected by the earthquake. At this time, the column section yields and the bending bearing capacity is . Because the axial force of the beam is very small, its axial force is set to zero.

The flexural bearing capacity of beam elements is calculated according to the “Code for Design of Concrete Structures” (GB) [9]. The load-bearing capacity of the column section is assumed to maintain the plane according to the section strain, and the stress-strain relationship between the steel and concrete specified in the concrete specification is used for calculation.

From Figure 4, the axial equilibrium equations are listed, i.e., . In the available form, the sum of the moments of the center point and the external force of the section is equal to zero, i.e., , available formula is given as follows:

In the formula, is the column axial force at the time of destruction, is the bending moment of the column at the time of destruction, is the height of concrete under pressure, is the area of the pressed steel bar and the yield stress, is tensile stress and area of tensile reinforcement, is the thickness of the concrete protective layer, is the concrete compressive stress, is the effective height of the column section, , and is the height of the concrete column section.

Shear Strength of Beam and Column Sections

The shear capacity of beam and column sections is generally considered to be provided by concrete and shear reinforcement . In the plastic hinge area, due to the repeated stress, the cracking of the concrete is very serious, so the part decreases with the increase of the ductility ratio. In addition, is also related to the existence of axial compressive stress. If the axial compressive stress is large, is effective. When the axial compressive stress is less than , can be regarded as zero. turns out to be , When the ductility ratio reaches the ductile capacity, it is reduced to . The value of is calculated as follows: where is the full cross-sectional area and is the axial force at which the section yields. Under the repeated action of the ground motion, the column unit may be stressed at a certain moment and may be pulled at another time, especially for the upper column unit of the structure. In order to reduce the influence of uncertainties, only the influence of the axial force of the column element on the shear capacity of the structure under the action of the gravity load representative value is considered. For ease of analysis, separate formulas for the shear capacity of beams and columns in the code are written separately. That is, represents the contribution of concrete to shear capacity and represents the contribution of reinforcing steel to shear capacity. Combined with the “Code for Design of Concrete Structures” (GB) [10], the formulas for rectangular beams and columns and are as follows. (1)For beams