Self-consolidating earth concrete (SCEC) addresses the long construction process of conventional earthen constructions and their structural limitations, while further efforts are needed to enhance its sustainability. This study explores the development of a kaolinite-based self-consolidating earth paste (SCEP) due to their blended powder system, incorporating raw and treated (calcined and ground-calcined) kaolinite under various activation techniques, such as water hydration, sodium hexametaphosphate (NaHMP), and sodium hydroxide (NaOH) activation. The synergistic effect of calcination and mechanosynthesis on rheological, mechanical, structural, and microstructural properties of SCEP were investigated. Mechanically treated kaolinite increased yield stress, plastic viscosity, storage modulus evolution, and build-up index, while delayed the strength development compared to the calcined kaolinite samples. Among the investigated activators, NaOH resulted in more promising structural build-up, storage modulus, and compressive strength development. These findings were elaborated with X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), calorimetry, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM).

Reconstructing fluvial dynamics is a fundamental requirement for understating the interaction between past environmental changes and human adaptation. This study focuses on the central part of the floodplain of the Nan River in northern Thailand that likely played a role in the catastrophic flood of 1818 CE, which damaged the ancient of Nan city and forced its relocation. We investigated nine sediment cores from the floodplain and from the eastern tributaries of the Nan River, to identify the potential source of floods in the past. By combining the analyses of sedimentary characteristics and provenance, the study reveals that the eastern tributaries were the dominant sediment source for most areas, with the Nan River only influencing areas close to its channel. According to optically stimulated luminescence dating, the highest sediment accumulation occurred during the eleventh to thirteenth centuries CE, coinciding with agricultural expansion and deforestation, suggesting increased erosion in the catchment of the tributaries. These findings challenge the assumption that the main Nan River has been the primary contributor to flooding catastrophes in the region and highlights the potential crucial role of smaller tributaries in similar settings in other parts of the globe.

In comparison with normally consolidated soft ground, consolidating soft ground often displays inferior engineering properties, which have not been thoroughly investigated yet. This study aims to investigate the undrained shear characteristics of consolidating soft soil under both compression and extension test conditions. A series of undrained triaxial compression and extension tests are conducted on reconstituted kaolin clay under different degrees of consolidation. The results indicate that the undrained stress-strain curves, the evolution of excess pore water pressure, and the undrained stress paths for both normally consolidated and consolidating soft soils exhibit a similar pattern. It is also found that consolidating soft soil also follows the Mohr-Coulomb failure criterion, which can be expressed in terms of effective consolidation pressure and is considered to be independent of degree of consolidation. The primary difference between normally consolidated and consolidating soft soils lies in the initial tangent modulus, which decreases as the degree of consolidation decreases. Subsequently, a modified Duncan-Chang constitutive model is developed to accurately approximate the measured stress-strain curves of consolidating soft soil. Finally, the proposed model is validated by the experimental data, demonstrating its capability to effectively capture the influence of the consolidating state on shear characteristics.

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.

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.

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.