This paper presents a data-driven model updating framework to estimate the operational parameters of a laterally-impacted pile. The goal is to facilitate the estimation of soil-pile interaction parameters such as the mobilized mass and stiffness, as well as geometrical data such as embedded pile length, using output-only information. Accurate knowledge of mass, stiffness, and pile embedded length is essential for understanding foundation behavior when developing digital-twin models of structures for the purpose of damage detection. The method first employs subspace identification to determine modal parameters and quantifies their uncertainties using output-only data. The covariance matrix adaptation evolution strategy (CMA-ES), a stochastic evolutionary algorithm, is subsequently used to update the model. The effectiveness of the approach is demonstrated through its application to numerical models in this paper, to quantify errors, and subsequently to data from a documented full-scale field test of a pile subjected to an impact load. The work underscores the potential of statistical updating in advancing the accuracy and reliability of soil-structure interaction parameter estimation for systems where only output data might exist.

Due to the time-dependent effect of rockfill dams, the conventional time-invariant finite element method (FEM) can hardly meet practical engineering requirements. This paper proposes an updating Bayesian FEM method for accurate long-term deformation analysis. A combined FEM model is introduced accounting for both instantaneous and creep behaviors. The FEM model is then updated using a Bayesian algorithm, unscented Kalman filter (UKF). The UKF calibrates the prior FEM predictions by incorporating real-time measurement data, thus iteratively reducing discrepancies between model predictions and actual observations. To further enhance the algorithm accuracy, a power-law-based fading memory factor is proposed to mitigate measurement noise in standard UKF. For parameter identification, a slice approach of the high-dimensional covariance confidence ellipsoid is developed. The methodology is validated in Qingyuan rockfill dam, in Guangdong province, China. Results show that the updated FEM is more consistent with the actual monitoring data. The fading memory improves standard UKF performance with a lower relative root-mean-square error (RRMSE). Additionally, the slice method reveals that a specific three-parameter configuration behaves better than the others. The proposed approach can also be extended to other fields including slope and tunneling.

Tunneling operations in modern construction demand meticulous evaluation of their impact on nearby structures. A primary concern is the potential for soil subsidence, which could damage adjacent buildings. Complicating matters is the challenge of accurately modeling such settlement and the consequent damage, a critical process for informed decision-making during construction projects. By employing Bayesian updating, we refine our models by acquiring posterior distributions for key parameters. We put forth an analytical method for profiling ground settlement and follow this by calculating the strain on an equivalent beam, which serves as a proxy for building damage. This results in a distribution of strain values that allows for an assessment of how varying certain length parameters affects the probability of maintaining a safe distance between the tunneling activities and the surrounding buildings. With this probabilistic approach, one can propose a recommended safety distance as a guideline for construction practices.

Heavy metal pollution is one of the most devastating abiotic factors, significantly damaging crops and human health. One of the serious problems it causes is a rise in cadmium (Cd) toxicity. Cd is a highly toxic metal with a negative biological role, and it enters plants via the soil-plant system. Cd stress induces a series of disorders in plants' morphological, physiological, and biochemical processes and initiates the inhibition of seed germination, ultimately resulting in reduced growth. Fiber crops such as kenaf, jute, hemp, cotton, and flax have high industrial importance and often face the issue of Cd toxicity. Various techniques have been introduced to counter the rising threats of Cd toxicity, including reducing Cd content in the soil, mitigating the effects of Cd stress, and genetic improvements in plant tolerance against this stress. For decades, plant breeders have been trying to develop Cd-tolerant fiber crops through the identification and transformation of novel genes. Still, the complex mechanism of Cd tolerance has hindered the progress of genetic breeding. These crops are ideal candidates for the phytoremediation of heavy metals in contaminated soils. Hence, increased Cd uptake, accumulation, and translocation in below-ground parts (roots) and above-ground parts (shoots, leaves, and stems) can help clean agricultural lands for safe use for food crops. Earlier studies indicated that reducing Cd uptake, detoxification, reducing the effects of Cd stress, and developing plant tolerance to these stresses through the identification of novel genes are fruitful approaches. This review aims to highlight the role of some conventional and molecular techniques in reducing the threats of Cd stress in some key fiber crops. Molecular techniques mainly involve QTL mapping and GWAS. However, more focus has been given to the use of transcriptome and TFs analysis to explore the potential genomic regions involved in Cd tolerance in these crops. This review will serve as a source of valuable genetic information on key fiber crops, allowing for further in-depth analyses of Cd tolerance to identify the critical genes for molecular breeding, like genetic engineering and CRISPR/Cas9.

The paper presents a study on the dredging vibrational effects, for nourishment purpose, on the existing structures surrounding the worksite. Nourishment is a common operation when beach (or coasts, or ports) protection is required, allowing to reduce far-field impacts of coastal structures and improve navigability. Nourishment is then performed to reshape underwater land, and it is usually practiced by locating in the zones in which is required, soil coming from nearby areas. This latter is often obtained by a dredging process, in which the phases of excavation, transportation and soil placement are carried out. From the structural point of view, of interest is the excavation phase, which is usually performed in the water environment by a ship equipped with a dredge that mines the seabed, generating a new source of vibrations for the existing structures facing the working area. The aim of this paper is to assess the effects of vibrations induced by dredging operations, by taking as reference the recently performed nourishment in the port of Bari, Southern Italy. To this scope, an existing structure was selected and identified as sentry building, considering its extreme proximity to the worksite. Hence, a structural monitoring was performed, by investigating the behaviour of the structure before, during and after the dredging. Three main controls were carried out within the monitoring campaign: (a) check of the vibration levels and comparison with thresholds provided by the current Italian prescriptions for human comfort and structural damages; (b) operational modal analysis to assess the possible variations of the structural behaviour during dredging; (c) calibration of a numerical model to simulate the structural behaviour of the sentry building and to derive unknown geometrical and mechanical parameters. A full description of the reference building (characterized by a certain irregularity degree) and all the monitoring phases are reported throughout the manuscript. The results show that, over the monitoring period, the dredging vibration levels never exceeded the thresholds provided by code provisions, and subsequently, the sentry building did not report structural damages, as confirmed by the continuous control of dynamic parameters from experimental and numerical models. In addition, the contents of the paper show the paramount importance of the structural health monitoring, and the experience herein reported can inspire the management of buildings under particular actions like the ones herein investigated.

Dynamic soil-structure interaction (SSI) is an important field in civil engineering with applications in earthquake engineering, structural dynamics, and structural health monitoring (SHM). There is an ongoing need for the development of numerical methods that can accurately estimate SSI parameters to model these systems. In this paper, a Frequency Response Function (FRF)-based model updating method is developed that can estimate the embedded length of foundation piles, in addition to the mobilized soil mass and stiffness, when a lateral impact load is applied. Knowledge of the embedded length of piles is very important for modelling foundation behaviour, and may not be readily available from as-built construction information. For example, if developing reference damage models or digital twins of foundation structures, full knowledge of the pile geometry is required. The work in this paper develops a two-stage iterative model updating method, which utilizes FRF data obtained at the pile ' s head as a result of an applied lateral impact load. The method uses information from the 1st mode of vibration to estimate the mobilised soil mass and stiffness, and subsequently uses information from the 2nd mode of vibration to estimate the embedded length. To appraise the approach, impact tests are numerically simulated on a number of 'piles ' (numerical spring-beam systems) with varying length/diameter ( L/D ) ratios to derive FRFs, whereby the models have known length and dynamic properties. These FRFs are then used as targets in the model updating approach, which iteratively varies the properties of a numerical model of a pile to obtain a match in the FRF data, and subsequently estimates the mobilised stiffness, mass, and embedded length. The results of the analyses illustrate that by minimising the difference in the first and second FRF peaks between the target and estimated FRFs, the method can accurately estimate the mass, stiffness and embedded length properties of the test 'piles ' . The performance of the approach against numerical case applications is assessed in this paper, as the properties of these systems are known in advance, facilitating quantification of the errors and performance. The developed method requires further validation through full-scale testing to confirm its effectiveness in real-world scenarios.

Reliable predictions of time-dependent diaphragm wall deflections in deep excavations in soft soils are crucial for managing potential damage to the surrounding environment. Bayesian updating offers a rational method for refining these predictions by using monitoring data. The inconsistency in monitoring data necessitates an examination of the impact of using different datasets on Bayesian updating. This paper presents a Bayesian updating of time-dependent deflections of diaphragm walls in deep excavations in soft soils using different datasets. The soft soil creep model is utilized to simulate the time-dependent behavior of soil. Bi-directional longshort memory neural networks are employed as surrogate models. Different updating strategies with varying numbers of data in the datasets are adopted for Bayesian updating and illustrated with the Taipei National Enterprise Center project. The results show that incorporating more monitoring data in the datasets for Bayesian updating does not guarantee better predictions unless the consistency of the monitoring data used is ensured. Additionally, the Bayesian updating process more accurately predicts short-term deflections than long-term ones, likely due to the higher consistency in short-term construction processes. It is advisable to review the construction processes to ensure the consistency of the monitoring data before selecting the appropriate dataset.

Background: As a broad-spectrum tetracycline antibiotic, Oxytetracycline (OTC) was widely used in a variety of applications. But, the overuse of OTC had led to the detection of it in food, water and soil, which could present significance risk to human health and cause damage to ecosystem. It was of great significance to develop sensitive detection methods for OTC. Herein, an environmentally friendly photoelectrochemical (PEC) aptasensor was constructed for the sensitive detection of OTC based on CuO-induced BiOBr/Ag 2 S/PDA (Polydopamine) photocurrent polarity reversal. Results: BiOBr/Ag 2 S/PDA composites modified electrode not only produced stable initial anodic photocurrent but also provided attachment sites for the aptamer S1 of OTC by the strong adhesion of PDA. On the other hand, CuO loaded OTC aptamer S2 (Cu -S2) was got through Cu -S bonds. After the target OTC was identified on the electrode surface, CuO was introduced to the surface of ITO/BiOBr/Ag 2 S/PDA through the specific binding of OTC to S2. This identification process formed dual Z -type heterojunctions and resulted in a remarkable reversal of photocurrent polarity from anodic to cathodic. Under optimization conditions, the PEC aptasensor showed a wide linear range (50 fM - 100 nM), low detection limit (1.9 fM), excellent selectivity, stability and reproducibility for the detection of OTC. Moreover, it was successfully used for the analysis of OTC in real samples of tap water, milk and honey, and had the potential for practical application. Significance: This work developed an environmentally friendly photocurrent-polarity-switching PEC aptasensor with excellent selectivity, reproducibility, stability, low LOD and wide linear range for OTC detection. This sensitive system, which was including BiOBr, Ag 2 S, PDA and CuO were low toxicity, not only reduced the risk of traditional toxic semiconductors to operators and the environment, but can also be used for the detection of real samples, broadening the wider range of applications for BiOBr, Ag 2 S, PDA and CuO.

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power -law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect -specific growth models. The framework is applicable regardless of the availability of matched or non -matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where timedependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes' theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.