JP3670356B2 - Compaction management system - Google Patents
Compaction management system Download PDFInfo
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- JP3670356B2 JP3670356B2 JP23530895A JP23530895A JP3670356B2 JP 3670356 B2 JP3670356 B2 JP 3670356B2 JP 23530895 A JP23530895 A JP 23530895A JP 23530895 A JP23530895 A JP 23530895A JP 3670356 B2 JP3670356 B2 JP 3670356B2
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Description
【０００１】
【発明の属する技術分野】
本発明は締固め管理システム、更に詳細には締固め対象物の締固め作業中においてリアルタイムでその締固め度（密度比）を的確に推定する締固め管理システムに関する。
【０００２】
【従来の技術】
従来から、土、ＲＣＤ用コンクリートなどの締固め対象物が、その締固め作業中においてどの程度まで締め固まっているかを知るために、木槌で型枠の外側を打撃し、その音を聴取することによって締固め度を判断する方法や、ブリージング水を目視することによって締固め度を判断する方法や、放射線発生端子を締固め後の締固め対象物に埋め込み、締固め対象物の表面に放射線測定部を設置し、そして締固め対象物を透過する放射線量を測定することによって実測締固め度（実測ＲＩ密度比）を判断するＲＩ法と称される方法などが採られている。
【０００３】
【発明が解決しようとする課題】
しかしながら、上述の木槌で打撃する方法や、ブリージング水を目視する方法では、測定者の主観で締固め度を判断するため、一定の締固め度が得られず、従って締固め対象物の品質が不均一になってしまうという欠点がある。
【０００４】
また、上記ＲＩ法では、例えばダムのＲＣＤ工法に用いる場合に、ＲＣＤ用コンクリート中に放射線発生端子を埋め込むために多大の労力を要し、そのため振動ローラーでＲＣＤ用コンクリートを転圧しながら、リアルタイムで締固め度を測定することは非常に困難であり、締固め度の過不足によりコンクリートの品質が不均一になり易いといった欠点がある。
【０００５】
本発明は上述の従来の技術の欠点に着目し、これを解決せんとしたものであり、その目的は、締固め対象物の締固め作業中においてリアルタイムでその締固め度を的確に推定することができる締固め管理システムを提供することにある。
【０００６】
【課題を解決するための手段】
本発明は上記の目的に鑑みてなされたものであり、その要旨とするところは、締固め対象物の締固め作業中においてリアルタイムでその締固め度を推定する締固め管理システムであって、上記締固め対象物の単位水量、沈下量、応答振動数、応答スペクトル、ひずみ率、 AE ヒット数、 AE カウント数及び AE エネルギーを断続的或いは連続的に測定する複数の入力因子と、該複数の入力因子にて測定した各種情報の経時的変化に基づいて前記締固め対象物の締固め度としてＲＩ密度比を推定するニューラルネットワーク処理手段とを備えることを特徴とする締固め管理システムにある。
【０００７】
この態様によれば、上記締め固め対象物の締固め作業中においてリアルタイムでその締固め度を的確に推定することができる。
【０００８】
【発明の実施の形態】
上述した本発明の締固め管理システムにおいて、締固め対象物としては、土、コンクリート等が挙げることができる。
【０００９】
また、上記各入力因子にて測定される現況情報としては、単位水量Ｗ(Kg/m3 )、沈下量Ｄ(mm)、応答振動数ωｇ(Hz)、応答スペクトルＡｇ(G )、ひずみ率εｑ、AE（Acoustic Emission ：アコースティック・エミッション ）ヒット数ＡＨ、AEカウント数ＡＣ、AEエネルギーＡＥ等を挙げることができる。
【００１０】
これらの各種現況を測定するために、例えば単位水量の現況を測定するための単位水量測定システム、沈下量を現況を測定するための電子スタッフESP-05A （日本光学(株)製）、応答振動数、応答スペクトル、ひずみ率の現況を測定するための加速度計、AEヒット数ＡＨやAEカウント数ＡＣやAEエネルギーＡＥの現況を測定するためのAEセンサー（日本フィジカルアコースティック（株）製）等適当な入力因子を用いる。
【００１１】
ニューラルネットワーク処理手段においては、入力層に相当する各入力因子にて測定された各種現況情報を入力信号として、各入力信号を各処理ユニットに入力し、各処理ユニットにおいては、重み付け総和処理と、しきい値処理とを行ない、この処理により算出された結果を各出力信号、即ち推定締固め度として出力する。上記ニューラルネットワーク処理手段としては、具体的には例えばパーソナルコンピュータPC-9801 シリーズ（日本電気（株）製）のノイマン型コンピュータ用の市販のニューロコンピューティングソフト「RHINE EX」システム（ＣＲＣセンチェリリサーチセンタ（株）製）等を用いることができる。
【００１２】
このように本発明の締固め管理システムでは、締固め対象物の締固め作業中において、複数の入力因子が上記締固め対象物の各種現況を断続的或いは連続的に測定し、この複数の入力因子にて測定した各種現況情報の経時的変化に基づいてニューラルネットワーク処理手段がその締固め度を推定する。
【００１３】
【実施例】
以下、本発明の実施例を添付図面に基づいて説明するが、本発明はこれに限定されるものではない。
【００１４】
本発明の締固め管理システムは、図１に示すように、ＲＣＤ用コンクリートの締固め作業中においてリアルタイムでその締固め度を推定する締固め管理システムであって、上記ＲＣＤ用コンクリートの各種現況を断続的或いは連続的に測定し、この各種現況情報を入力信号とする該システムの入力ユニットに相当する８つの入力因子と、該複数の入力因子にて測定した各種現況情報の経時的変化に基づいてその締固め度を推定するニューラルネットワーク処理手段としてのパーソナルコンピュータPC-9801 シリーズ（日本電気（株）製）及びニューロコンピューティングソフト「RHINE EX」システムとを備えてなる。
【００１５】
入力因子としては、単位水量Ｗ(Kg/m3 )、沈下量Ｄ(mm)、応答振動数ωｇ(Hz)、応答スペクトルＡｇ(G )、ひずみ率εｑ、AEヒット数ＡＨ、AEカウント数ＡＣ、及びAEエネルギーＡＥを用いており、ＲＣＤ用コンクリートの各種現況を断続的或いは連続的に測定している。
【００１６】
上記「RHINE EX」システムには、まず上記各入力因子にて測定された各種現況情報が入力信号として入力される（入力層）。そして入力された入力信号に対する重み付け総和処理と、及びしきい値処理とを複数の各処理ユニット（中間層）にて行ない、ここで算出された処理信号が出力信号として出力される（出力層）。
【００１７】
上記演算処理は、具体的には下記の【数１】によって行なう。
【００１８】
【数１】
各処理ユニットにおける演算処理では、上記の【数１】の重み付け係数wi及びしきい値θの値が各々異なる値に設定されている。また、上記重み付け係数wi、及びしきい値θは、バックプロパゲーション法によって適時調整するようにしている。
【００２０】
出力信号、即ち推定締固め度は、ディスプレイ等によって簡易に確認することができる。
【００２１】
図２は本発明の締固め管理システムによる推定締固め度（推定ＲＩ密度比）と、実測締固め度（実測ＲＩ密度比）との関係を示すグラフ、図３は図２の実測締固め度から推定締固め度を差し引いた値（残差）の分布を示すグラフである。
【００２２】
図２に示すように、本発明の締固め管理システムにおける推定締固め度、即ち推定ＲＩ密度比と、従来のＲＩ法にて測定した実測締固め度、即ち実測ＲＩ密度比とは、共に同様の傾向にあり、本発明の締固め管理システムにおいて締固め度の推定が的確に行なわれることは明らかである。また、上述した残差分布は、図３に示すように、正規分布形状をしており、この標準偏差は１．１９％である。
【００２３】
このように、本発明の締固め管理システムでは、ＲＣＤ用コンクリートの締固め作業中においてリアルタイムでその締固め度を的確に推定することができる。
【００２４】
【発明の効果】
本発明の締固め管理システムによれば、締固め作業中において、複数の入力因子によって締固め対象物の各種現況を断続的或いは連続的に測定し、この複数の入力因子にて測定した各種現況情報の経時的変化に基づき、ニューラルネットワーク処理手段によってその締固め度を推定するようにしたので、締固め対象物の締固め作業中においてリアルタイムでその締固め度を的確に推定することができる。
【図面の簡単な説明】
【図１】本発明の締固め管理システムの概念図である。
【図２】本発明の締固め管理システムによる推定締固め度（推定ＲＩ密度比）と、実測締固め度（実測ＲＩ密度比）との関係を示すグラフである。
【図３】図２の実測締固め度から推定締固め度を差し引いた値（残差）の分布を示すグラフである。
【符号の説明】
Ｗ 単位水量
Ｄ 沈下量
ωｇ 応答振動数
Ａｇ 応答スペクトル
εｑ ひずみ率
ＡＨ AEヒット数
ＡＣ AEカウント数
ＡＥ AEエネルギー[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a compaction management system, and more particularly to a compaction management system for accurately estimating the degree of compaction (density ratio) in real time during compaction of a compacted object.
[0002]
[Prior art]
Conventionally, in order to know how much compacted objects such as soil and concrete for RCD are compacted during the compaction work, the outside of the formwork is hit with a mallet and the sound is heard. The method of judging the degree of compaction by the method, the method of judging the degree of compaction by observing the breathing water, and the radiation generating terminal is embedded in the compacted object after compaction, and radiation is applied to the surface of the compacted object. A method called RI method is adopted in which a measurement unit is installed and an actual compaction degree (actual RI density ratio) is determined by measuring a radiation dose transmitted through the compacted object.
[0003]
[Problems to be solved by the invention]
However, in the method of hitting with a wooden mallet or the method of visually observing breathing water, the degree of compaction is judged by the subjectivity of the measurer, so a certain degree of compaction cannot be obtained, and therefore the quality of the compacted object. Has the disadvantage of becoming non-uniform.
[0004]
In addition, in the above RI method, for example, when used in the RCD construction method of a dam, a great deal of labor is required to embed the radiation generating terminal in the RCD concrete, so that the RCD concrete is compacted with a vibrating roller in real time. It is very difficult to measure the degree of compaction, and there is a drawback that the quality of the concrete tends to be uneven due to excessive or insufficient compaction.
[0005]
The present invention focuses on the drawbacks of the above-described conventional technology and solves this problem. The purpose of the present invention is to accurately estimate the degree of compaction in real time during the compaction operation of the compacted object. It is to provide a compaction management system that can do this.
[0006]
[Means for Solving the Problems]
The present invention has been made in view of the above object, and the gist of the present invention is a compaction management system for estimating the degree of compaction in real time during compaction of a compacted object. Multiple input factors that measure intermittently or continuously the unit water volume, sinking volume, response frequency, response spectrum, strain rate, AE hit count, AE count count, and AE energy of the compacted object, and the multiple inputs based on the time course of various information measured by factors in compaction management system characterized by obtaining Bei the neural network processing means for estimating the RI density ratio as degree of compaction of the compaction object.
[0007]
According to this aspect, the degree of compaction can be accurately estimated in real time during the compacting operation of the compacted object.
[0008]
DETAILED DESCRIPTION OF THE INVENTION
In the compaction management system of the present invention described above, examples of compaction objects include soil and concrete.
[0009]
The current status information measured by the above input factors includes unit water volume W (Kg / m 3 ), settlement amount D (mm), response frequency ωg (Hz), response spectrum Ag (G), strain rate. εq, AE (Acoustic Emission) hit number AH, AE count number AC, AE energy AE, and the like.
[0010]
In order to measure these various current conditions, for example, a unit water volume measurement system for measuring the current status of unit water volume, electronic staff ESP-05A (manufactured by Nippon Optical Co., Ltd.) for measuring the current status of subsidence, response vibration Accelerometer for measuring the current number, response spectrum, strain rate, AE sensor for measuring the current state of AE hit number AH, AE count number AC, and AE energy AE (Nippon Physical Acoustics) suitable Use simple input factors.
[0011]
In the neural network processing means, various current status information measured by each input factor corresponding to the input layer as an input signal, each input signal is input to each processing unit, and in each processing unit, weighted summation processing, Threshold processing is performed, and the result calculated by this processing is output as each output signal, that is, an estimated degree of compaction. Specific examples of the neural network processing means include a commercially available neurocomputing software “RHINE EX” system (CRC Sentry Research Center) for Neumann computers of the personal computer PC-9801 series (manufactured by NEC Corporation). Etc.) can be used.
[0012]
As described above, in the compaction management system of the present invention, during the compacting operation of the compacted object, a plurality of input factors intermittently or continuously measure various current states of the compacted object, and the plurality of inputs. The neural network processing means estimates the degree of compaction based on the temporal changes of various current status information measured by factors.
[0013]
【Example】
Hereinafter, although the example of the present invention is described based on an accompanying drawing, the present invention is not limited to this.
[0014]
As shown in FIG. 1, the compaction management system of the present invention is a compaction management system for estimating the compaction degree in real time during the compaction work of the concrete for RCD. Based on 8 input factors corresponding to the input unit of the system that measures intermittently or continuously and uses the various current status information as input signals, and changes over time of the various current status information measured by the multiple input factors. PC-9801 series (manufactured by NEC Corporation) and neural computing software “RHINE EX” system as neural network processing means for estimating the degree of compaction.
[0015]
As input factors, unit water amount W (Kg / m 3 ), settlement amount D (mm), response frequency ωg (Hz), response spectrum Ag (G), strain rate εq, AE hit number AH, AE count number AC AE energy AE is used, and various current conditions of RCD concrete are measured intermittently or continuously.
[0016]
In the “RHINE EX” system, first, various current status information measured by the above input factors are input as input signals (input layer). Then, weighted summation processing and threshold value processing for the input signal are performed in each processing unit (intermediate layer), and the processing signal calculated here is output as an output signal (output layer) .
[0017]
Specifically, the above arithmetic processing is performed by the following equation (1).
[0018]
[Expression 1]
In the arithmetic processing in each processing unit, the values of the weighting coefficient wi and the threshold value θ of the above equation (1) are set to different values. The weighting coefficient wi and the threshold value θ are adjusted in a timely manner by a back propagation method.
[0020]
The output signal, that is, the estimated degree of compaction can be easily confirmed by a display or the like.
[0021]
FIG. 2 is a graph showing the relationship between the estimated degree of compaction (estimated RI density ratio) by the compaction management system of the present invention and the actual compaction degree (actual RI density ratio), and FIG. 3 is the actual compaction degree of FIG. It is a graph which shows distribution of the value (residual) which deducted the estimated compaction degree from.
[0022]
As shown in FIG. 2, the estimated compaction degree, that is, the estimated RI density ratio in the compaction management system of the present invention is the same as the actually measured compaction degree measured by the conventional RI method, that is, the actually measured RI density ratio. It is obvious that the degree of compaction is accurately estimated in the compaction management system of the present invention. Further, the above-described residual distribution has a normal distribution shape as shown in FIG. 3, and the standard deviation is 1.19%.
[0023]
Thus, in the compaction management system of the present invention, the compaction degree can be accurately estimated in real time during the compaction work of the concrete for RCD.
[0024]
【The invention's effect】
According to the compaction management system of the present invention, during the compaction operation, various current states of the object to be compacted are measured intermittently or continuously by a plurality of input factors, and various current states measured by the plurality of input factors. Since the degree of compaction is estimated by the neural network processing means based on the change in information over time, the degree of compaction can be accurately estimated in real time during the compacting operation of the compacted object.
[Brief description of the drawings]
FIG. 1 is a conceptual diagram of a compaction management system of the present invention.
FIG. 2 is a graph showing the relationship between an estimated compaction degree (estimated RI density ratio) and an actual compaction degree (actual RI density ratio) by the compaction management system of the present invention.
3 is a graph showing a distribution of values (residuals) obtained by subtracting the estimated compaction degree from the actually measured compaction degree of FIG. 2;
[Explanation of symbols]
W Unit water amount D Settlement amount ωg Response frequency Ag Response spectrum εq Distortion rate AH AE hit number AC AE count number AE AE energy
Claims (1)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP23530895A JP3670356B2 (en) | 1995-09-13 | 1995-09-13 | Compaction management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP23530895A JP3670356B2 (en) | 1995-09-13 | 1995-09-13 | Compaction management system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| JPH0978521A JPH0978521A (en) | 1997-03-25 |
| JP3670356B2 true JP3670356B2 (en) | 2005-07-13 |
Family
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP23530895A Expired - Fee Related JP3670356B2 (en) | 1995-09-13 | 1995-09-13 | Compaction management system |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JP3670356B2 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6884260B1 (en) * | 2020-10-30 | 2021-06-09 | 前田道路株式会社 | Rolling machine and density estimation system for asphalt mixture |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4404494B2 (en) * | 2001-02-19 | 2010-01-27 | 株式会社奥村組 | Concrete compaction judgment method and apparatus |
| JP7219910B2 (en) * | 2018-09-07 | 2023-02-09 | 学校法人金沢工業大学 | Optimal Compaction Judgment Construction System for Concrete |
| JP7097274B2 (en) * | 2018-09-28 | 2022-07-07 | 大和ハウス工業株式会社 | Ground prediction system |
-
1995
- 1995-09-13 JP JP23530895A patent/JP3670356B2/en not_active Expired - Fee Related
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6884260B1 (en) * | 2020-10-30 | 2021-06-09 | 前田道路株式会社 | Rolling machine and density estimation system for asphalt mixture |
| JP2022072640A (en) * | 2020-10-30 | 2022-05-17 | 前田道路株式会社 | Rolling machine and asphalt mixture density estimation system |
Also Published As
| Publication number | Publication date |
|---|---|
| JPH0978521A (en) | 1997-03-25 |
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