JP5967417B2 - Building health check method - Google Patents
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Description
本発明は、建物の健全性を確認するための方法に関する。 The present invention relates to a method for checking the health of a building.
建物にセンサを設置し、このセンサからの情報に基づいて建物の損傷、劣化の度合いを把握し、建物の損傷検知や健全性評価を行う構造ヘルスモニタリングが注目されている（例えば、特許文献１、特許文献２参照）。特に、オフィスビルやマンション等の多層構造の建物においては、地震が発生した際に、その被災状況を早期に且つ精度よく確認（把握）、判定することが求められる。 Attention has been focused on structural health monitoring in which a sensor is installed in a building, the degree of damage and deterioration of the building is grasped based on information from the sensor, and damage detection and soundness evaluation of the building are performed (for example, Patent Document 1). , See Patent Document 2). Particularly in multi-layered buildings such as office buildings and condominiums, when an earthquake occurs, it is required to confirm (understand) and determine the damage status quickly and accurately.
また、建物に多数のセンサを設置し、地震時の建物の各階（層）の応答、さらに建物全体の応答を把握することが好ましいが、現実的には経済性などの制約から多数のセンサを設置することが難しい。このため、建物の限られた階にセンサを設置することが一般的であり、これに伴い、地震時に、この限られた階のセンサで取得した情報から建物の各階の応答を精度よく推定する手法が強く求められている。 In addition, it is preferable to install a large number of sensors in the building and grasp the response of each floor (layer) of the building at the time of the earthquake, and further the response of the entire building. It is difficult to install. For this reason, it is common to install a sensor on a limited floor of a building, and accordingly, at the time of an earthquake, the response of each floor of the building is accurately estimated from information acquired by the sensor on the limited floor There is a strong demand for methods.
これに対し、非特許文献１には、地震観測データとＡＲＸモデルを用いて、観測されていない階の応答を近似的に推定する方法が開示されている。この方法では、まず、建物の設計モデル解析モデルのモード形と同定された観測階（センサ設置階）の刺激関数から各階の刺激関数を振動モードごとに決定する。次に、刺激関数と同定された極から、各階の変位応答を出力とするＡＲＸモデルの留数を求め、さらに、各階変位を出力とするＡＲＸモデルの外生入力パラメータを求めるようにしている。これにより、層間変位や層間変形角を求めることができ、地震による被災状況を把握し、建物の耐震性能評価を行うことができる。
On the other hand, Non-Patent
しかしながら、上記の地震観測データとＡＲＸモデルを用いて、観測されていない階の応答を近似的に推定する方法においては、設計情報と実際の建物の特性との間に必ず存在する差異が反映されていない。このため、推定の精度が悪くなり、健全性、耐震性評価の信頼性を損なうおそれがあった。 However, in the method of approximately estimating the response of unseen floors using the above-mentioned seismic observation data and ARX model, the difference that always exists between the design information and the actual building characteristics is reflected. Not. For this reason, there is a risk that the accuracy of estimation is deteriorated and the reliability of soundness and earthquake resistance evaluation is impaired.
本発明は、上記事情に鑑み、限られた階に設置したセンサで得られた建物の地震時応答情報に基づいて、より精度よく建物各階の応答を推定することを可能にする建物の健全性確認方法を提供することを目的とする。 In view of the above circumstances, the present invention is based on building earthquake response information obtained by sensors installed on limited floors, and can more accurately estimate the response of each floor of the building. The purpose is to provide a confirmation method.
上記の目的を達するために、この発明は以下の手段を提供している。 In order to achieve the above object, the present invention provides the following means.
本発明の建物の健全性確認方法は、多層構造の建物の健全性を確認するための方法であって、任意に設定した前記建物の観測層にセンサを設置し、地震時に前記センサで取得した前記観測層の応答情報に基づき、ベイズの定理を用いて前記建物の設計モデルの情報を更新した更新設計モデルを得て、前記観測層の応答情報と前記更新設計モデルに基づいて、前記建物の各層の応答を推定するようにし、前記更新設計モデルを学習的に更新するようにしたことを特徴とする。 The soundness confirmation method for a building according to the present invention is a method for confirming the soundness of a building having a multi-layer structure, and a sensor is installed on an observation layer of the building that is arbitrarily set, and is acquired by the sensor during an earthquake. Based on the response information of the observation layer, an updated design model obtained by updating the information on the design model of the building using Bayes' theorem is obtained, and based on the response information of the observation layer and the updated design model, the building The response of each layer is estimated, and the update design model is updated in a learning manner .
本発明の建物の健全性確認方法においては、ある地震時に、限られた観測層に設置したセンサで取得した建物の地震時応答情報に基づいて建物の設計モデルの情報（パラメータ）を学習的に修正（更新）し、この修正したモデルの情報を用いて建物の各層（各階）の応答を推定するようにしたことで、より精度よく建物各層の応答を推定することが可能になり、信頼性の高い健全性、耐震性評価を行うことが可能になる。 In the building health check method of the present invention, the information (parameters) of the design model of the building is learned by learning based on the earthquake response information of the building acquired by a sensor installed in a limited observation layer during a certain earthquake. By modifying (updating) and estimating the response of each layer (each floor) of the building using the information of the modified model, it becomes possible to estimate the response of each layer of the building with higher accuracy, and reliability. High soundness and earthquake resistance can be evaluated.
以下、図１及び図２を参照し、本発明の一実施形態に係る建物の健全性確認方法について説明する。 Hereinafter, with reference to FIG.1 and FIG.2, the soundness confirmation method of the building which concerns on one Embodiment of this invention is demonstrated.
ここで、本実施形態の建物の健全性確認方法は、学習型応答推定機能を有する構造ヘルスモニタリングシステムを用いてオフィスビルやマンション等の多層構造の建物の健全性を確認、把握するための方法に関するものである。 Here, the soundness confirmation method for a building according to the present embodiment is a method for confirming and grasping the soundness of a multi-layered building such as an office building or an apartment using a structural health monitoring system having a learning type response estimation function. It is about .
具体的に、本実施形態の建物の健全性確認方法においては、まず、設計モデルの質量行列Ｍ、減衰係数行列Ｃ、剛性行列Ｋが与えられており、式（１）に示す一般固有値問題を解いてｊ次の固有角振動数ｗｊと刺激関数φｊが得られている。 Specifically, in the soundness confirmation method for a building of the present embodiment, first, a mass matrix M, an attenuation coefficient matrix C, and a stiffness matrix K of a design model are given, and the general eigenvalue problem shown in Equation (1) is solved. The j-th order natural angular frequency w j and the stimulation function φ j are obtained by solving.
ここで、剛性分布ｋを修正する関数△ｋ（θ）を導入する。これにより、剛性分布がｋ’＝ｋ＋△ｋ（θ）に修正され、これに対応して剛性行列ＫがＫ’（θ）に修正される。このとき、モデルパラメータは、式（２）に示すように確率変数である（ｎｐはパラメータ数）。 Here, a function Δk (θ) for correcting the stiffness distribution k is introduced. Thereby, the stiffness distribution is corrected to k ′ = k + Δk (θ), and the stiffness matrix K is corrected to K ′ (θ) correspondingly. At this time, the model parameter is a random variable as shown in Expression (2) (n p is the number of parameters).
一方、センサ設置階（観測層）の建物応答絶対角度ｙｐ（θ）は式（３）で表され、この建物応答絶対加速度の確率モデルは式（４）で表せる。 On the other hand, the building response absolute angle y p (θ) of the sensor installation floor (observation layer) is expressed by Equation (3), and the probability model of this building response absolute acceleration can be expressed by Equation (4).
ｎｓは、建物に設置されたセンサの数（地動計測用のものを除く）、ｙｐ（上に^（ハット））（θ）は、Ｍ、Ｃ、Ｋ’（θ）で規定される修正設計モデルに観測された地動ｕを入力したときの各時刻におけるセンサ設置階の応答絶対加速度であり、その値を期待値として等しい分散σｙ ２で独立に正規分布していることを示している。 n s (except those for ground motion measurement) The number of sensors installed in a building, y p (^ (hat) on) (theta) is defined M, C, in K '(theta) This is the absolute response acceleration of the sensor installation floor at each time when the observed ground motion u is input to the modified design model, and shows that it is normally distributed independently with the same variance σ y 2 as the expected value. Yes.
そして、地震時に、式（５）で表す観測データDが得られると、ベイズの定理によってθの事後分布が式（６）で求められる。 Then, when observation data D represented by Equation (5) is obtained during an earthquake, the posterior distribution of θ is obtained by Equation (6) by Bayes' theorem.
ここで、ｐ（θ）は、事前分布で、式（７）のような互いに独立で平均が０の正規分布である。また、ｐ（D|θ）は、尤度関数で、式（８）で求められる。 Here, p (θ) is a prior distribution, and is a normal distribution that is independent from each other and has an average of 0 as in Expression (7). Further, p (D | θ) is a likelihood function, and is obtained by Expression (8).
このようにして得られる事後分布ｐ（θ|D）を最大化するθをθＭＡＰ（上に^（ハット））とすると、θＭＡＰ（^）によって修正された剛性行列Ｋ’（ θＭＡＰ（^））から、式（１）と同様の固有値問題を解いて、対応する刺激関数φｊ’が得られる。 Thus posteriori obtained distribution p | When the theta maximizing (θ D) θ MAP (^ ( hat) above), theta MAP (^) is modified by a stiffness matrix K '(θ MAP ( From ^)), the same eigenvalue problem as in equation (1) is solved to obtain the corresponding stimulus function φ j ′.
これは、事前情報である設計モデルを実際の観測データに基づいて、より現実に近づけるように更新したことを意味する。なお、この更新した事後分布ｐ（θ|Ｄ）を次回の地震に対する事前分布として用いることで継続的な学習が可能になる。 This means that the design model, which is prior information, has been updated to be closer to reality based on actual observation data. In addition , continuous learning becomes possible by using this updated posterior distribution p (θ | D) as a prior distribution for the next earthquake.
そして、建物の応答に支配的な影響を与えるモードを１〜ｎｍ次とすると、センサ設置階の応答絶対加速度は、式（９）で近似できる。 And if the mode which has a dominant influence on the response of a building is 1 to nm , the response absolute acceleration on the sensor installation floor can be approximated by equation (9).
Ｄは、Ｄ＝[１・・・１]Ｔ∈Ｒｎｓであり、Фｐは、Ф＝[φ1’・・・φnm’]からセンサ設置階に対応した行を抜き出した行列であり、ｑは、ｑ＝[ｑ1(t)・・・ｑnm(t)]Ｔで表される１〜ｎｍ次のモード応答相対加速度ベクトルである。すると、観測応答波形ｙｐ（上に〜（チルダ））からモード応答相対加速度の推定値ｑ（^）が式（１０）で得られる。 D is D = [1... 1] T ∈ R ns , and Ф p is a matrix obtained by extracting the row corresponding to the sensor installation floor from Ф = [φ 1 '... Φ nm ']. , q is q = [q 1 (t) ··· q nm (t)] 1~n m order mode response relative acceleration vector represented by T. Then, the observed response waveform y p estimate of modal response relative acceleration from (~ (tilde) above) q (^) is obtained by Equation (10).
Фｐ ＋はФｐの一般化逆行列である。これにより、全層の応答ｙ∈Ｒｎｆ（ｎｆは建物層数）が式（１１）で推定できる。 Ф p + is a generalized inverse matrix of ｐ p . Thereby, the response y∈R nf (n f is the number of building layers) of all layers can be estimated by the equation (11).
なお、Ｄ’＝[１・・・１]Ｔ∈Ｒｎｆである。また、式（１０）で一般化逆行列を用いていることにより、推定に使用する主要モードの数を任意に設定することが可能になっている。 Note that D ′ = [1... 1] T ∈ R nf . Further, by using the generalized inverse matrix in Expression (10), the number of main modes used for estimation can be arbitrarily set.
ここで、上記の本実施形態の建物の健全性確認方法を１５階建ての建物モデルに適用して行なったシミュレーションの結果について説明する。 Here, the result of the simulation performed by applying the building soundness confirmation method of the present embodiment to the 15-story building model will be described.
このシミュレーションでは、図１に示すように、多層構造の建物Ｔの１階と６階と１１階とＲ階に加速度センサ１を設置している。また、１階の加速度センサ１が地動加速度を計測し、建物応答絶対加速度が計測されるのは６階、１１階、Ｒ階のみとなっている。
In this simulation, as shown in FIG. 1,
そして、建物モデルに地動を入力し、応答解析を行なって各層の絶対加速度応答などを計算した結果を真値とした。また、センサによって取得したセンサ設置階（１階と６階と１１階とＲ階）のみの波形を用い、本発明によって全層の絶対加速度波形を推定した。さらに、センサによって取得したセンサ設置階のみの波形を用い、従来の手法によって、当初の設計モデルから全層の絶対加速度波形を推定した。そして、真値と、本発明による全層の推定値と、従来の手法による全層の推定値とを比較して、本発明の優位性を評価した。 Then, the ground motion was input to the building model, the response analysis was performed, and the absolute acceleration response of each layer was calculated as the true value. Moreover, the absolute acceleration waveform of all the layers was estimated by this invention using the waveform of only the sensor installation floor (1st floor, 6th floor, 11th floor, and R floor) acquired by the sensor. Furthermore, the absolute acceleration waveform of all layers was estimated from the original design model by the conventional method using the waveform of only the sensor installation floor acquired by the sensor. Then, the superiority of the present invention was evaluated by comparing the true value, the estimated value of all layers according to the present invention, and the estimated value of all layers by the conventional method.
図２は、シミュレーションの結果を示しており、図２（ａ）は、応答解析による真値と、従来の手法と、本発明の手法の各ケースの建物の刺激関数を３次まで示したものである。この図２（ａ）から、従来の手法を用いた場合には、真値と大きく異なっているのに対し、本発明の手法を用いた場合には、観測データに基づき、設計データが学習的に更新されているため、真値とほぼ一致する結果が得られることが確認された。 FIG. 2 shows the result of the simulation, and FIG. 2 (a) shows the true value by response analysis, the conventional method, and the stimulus function of the building in each case of the method of the present invention up to the third order. It is. From FIG. 2 (a), when the conventional method is used, it is greatly different from the true value, whereas when the method of the present invention is used, the design data is learned based on the observation data. because it is updated to, it was confirmed that results substantially coincident with the true value is obtained.
また、図２（ｂ）、図２（ｃ）は、全層の絶対加速度の時刻歴を用いて、各階（層）の最大絶対加速度、最大層間変形を求めた結果を示している。この結果から、従来の手法では、最大絶対加速度、最大層間変形ともに、真値と大きく異なってしまっているのに対し、本発明の手法を用いた場合には、全ての階で最大絶対加速度、最大層間変形の推定値と真値がよく一致しており、極めて効果的な応答推定が可能であることが実証された。 2B and 2C show the results of obtaining the maximum absolute acceleration and the maximum interlayer deformation of each floor (layer) using the time history of the absolute acceleration of all layers. From this result, in the conventional method, both the maximum absolute acceleration and the maximum interlayer deformation are greatly different from the true value, but when the method of the present invention is used, the maximum absolute acceleration, The estimated value of maximum interlaminar deformation and the true value are in good agreement, demonstrating that an extremely effective response estimation is possible.
したがって、本実施形態の建物の健全性確認方法においては、ある地震時に、限られた観測層に設置したセンサで取得した建物の地震時応答情報に基づいて建物の設計モデルの情報（パラメータ）を学習的に修正（更新）し、この修正したモデルの情報を用いて建物の各層（各階）の応答を推定するようにしたことで、より精度よく建物各層の応答を推定することが可能になり、信頼性の高い健全性、耐震性評価を行うことが可能になる。
また、本実施形態の建物の健全性確認方法においては、ある地震時に、限られた観測層に設置したセンサで取得した建物の地震時応答情報に基づいて建物の設計モデルの情報を学習的に更新し、後の地震時にセンサで取得した建物の応答状況に基づいて建物の各層（各階）の応答を推定するようにして、より精度よく建物各層の応答を推定することも可能であり、このようにして信頼性の高い健全性、耐震性評価を行うことが可能になる。
Therefore, in the building health check method of this embodiment, the information (parameter) of the design model of the building is obtained based on the earthquake response information of the building acquired by a sensor installed in a limited observation layer in a certain earthquake. It is possible to estimate the response of each layer of the building with higher accuracy by correcting (updating) learning and estimating the response of each layer (each floor) of the building using the information of the corrected model. Highly reliable soundness and earthquake resistance can be evaluated.
In the building health check method according to the present embodiment, the information on the building design model is learned in the event of an earthquake based on the earthquake response information of the building acquired by sensors installed in a limited observation layer. updating, so as to estimate the response of the building of each layer (each floor) on the basis of the response status of buildings acquired by the sensor during an earthquake after, it is also possible to estimate more accurately the building layers responses, this In this way, it is possible to perform highly reliable soundness and earthquake resistance evaluation.
以上、本発明に係る建物の健全性確認方法の一実施形態について説明したが、本発明は上記の実施形態に限定されるものではなく、その趣旨を逸脱しない範囲で適宜変更可能である。 As mentioned above, although one Embodiment of the soundness confirmation method of the building which concerns on this invention was described, this invention is not limited to said embodiment, In the range which does not deviate from the meaning, it can change suitably.
１ センサ
Ｔ 建物
1 Sensor T Building
Claims (1)
任意に設定した前記建物の観測層にセンサを設置し、地震時に前記センサで取得した前記観測層の応答情報に基づき、ベイズの定理を用いて前記建物の設計モデルの情報を更新した更新設計モデルを得て、前記観測層の応答情報と前記更新設計モデルに基づいて、前記建物の各層の応答を推定するようにし、前記更新設計モデルを学習的に更新するようにしたことを特徴とする建物の健全性確認方法。 A method for confirming the soundness of a multi-layered building,
An updated design model in which a sensor is installed in the observation layer of the building that is arbitrarily set, and information on the design model of the building is updated using Bayes' theorem based on response information of the observation layer acquired by the sensor during an earthquake The building is characterized in that the response of each layer of the building is estimated based on the response information of the observation layer and the updated design model, and the updated design model is updated learning. How to check the soundness of
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| JP6609403B2 (en) * | 2014-07-08 | 2019-11-20 | 株式会社Ｎｔｔファシリティーズ | Structure verification system, structure verification device, structure verification program |
| JP2016075583A (en) * | 2014-10-07 | 2016-05-12 | 清水建設株式会社 | System and method for confirming soundness of building |
| JP2016105050A (en) * | 2014-12-01 | 2016-06-09 | 清水建設株式会社 | Building soundness determination system and building soundness determination method |
| JP6464484B2 (en) * | 2015-06-22 | 2019-02-06 | 清水建設株式会社 | Building response estimation method |
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| JP7054641B2 (en) * | 2018-03-30 | 2022-04-14 | 株式会社Ｎｔｔドコモ | Structure characterization system |
| JP2019207128A (en) * | 2018-05-28 | 2019-12-05 | 清水建設株式会社 | Building damage detection device, building damage detection model learning device, building damage detection method, building damage detection model learning method, and program |
| JP2020008332A (en) * | 2018-07-03 | 2020-01-16 | 清水建設株式会社 | Building damage escalation detection device, building damage escalation detection model learning device, building damage escalation method, building damage escalation detection model learning method and program |
| JP7341414B2 (en) * | 2020-03-13 | 2023-09-11 | 五洋建設株式会社 | Damage degree prediction device, learned model, and learned model generation method |
| JP7482001B2 (en) | 2020-11-09 | 2024-05-13 | 株式会社東芝 | Plant evaluation system, method, and program |
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